2877 lines
1.0 MiB
2877 lines
1.0 MiB
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "be3c1872",
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"metadata": {},
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"source": [
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"# AI-based OCR Benchmark Notebook\n",
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"\n",
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"This notebook benchmarks **AI-based OCR models** on scanned PDF documents/images in Spanish.\n",
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"It excludes traditional OCR engines like Tesseract that require external installations."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "6a1e98fe",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Requirement already satisfied: pip in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (25.3)\n",
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"Note: you may need to restart the kernel to use updated packages.\n",
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"Requirement already satisfied: arrow>=0.15.0 in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from isoduration->jsonschema[format-nongpl]>=4.18.0->jupyter-events>=0.11.0->jupyter-server<3,>=2.4.0->jupyterlab->jupyter) (1.4.0)\n",
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"Requirement already satisfied: tzdata in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from arrow>=0.15.0->isoduration->jsonschema[format-nongpl]>=4.18.0->jupyter-events>=0.11.0->jupyter-server<3,>=2.4.0->jupyterlab->jupyter) (2025.2)\n",
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"Note: you may need to restart the kernel to use updated packages.\n",
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"Requirement already satisfied: executing>=1.2.0 in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from stack_data>=0.6.0->ipython>=6.1.0->ipywidgets) (2.2.1)\n",
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"Requirement already satisfied: asttokens>=2.1.0 in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from stack_data>=0.6.0->ipython>=6.1.0->ipywidgets) (3.0.1)\n",
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"Note: you may need to restart the kernel to use updated packages.\n",
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"Requirement already satisfied: ipython-pygments-lexers>=1.0.0 in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from ipython>=7.23.1->ipykernel) (1.1.1)\n",
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"Requirement already satisfied: parso<0.9.0,>=0.8.4 in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from jedi>=0.18.1->ipython>=7.23.1->ipykernel) (0.8.5)\n",
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"Requirement already satisfied: python-dateutil>=2.8.2 in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from jupyter-client>=8.0.0->ipykernel) (2.9.0.post0)\n",
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"Requirement already satisfied: platformdirs>=2.5 in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from jupyter-core!=5.0.*,>=4.12->ipykernel) (4.5.1)\n",
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"Requirement already satisfied: six>=1.5 in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from python-dateutil>=2.8.2->jupyter-client>=8.0.0->ipykernel) (1.17.0)\n",
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"Requirement already satisfied: executing>=1.2.0 in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from stack_data>=0.6.0->ipython>=7.23.1->ipykernel) (2.2.1)\n",
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"Requirement already satisfied: asttokens>=2.1.0 in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from stack_data>=0.6.0->ipython>=7.23.1->ipykernel) (3.0.1)\n",
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"Requirement already satisfied: pure-eval in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from stack_data>=0.6.0->ipython>=7.23.1->ipykernel) (0.2.3)\n",
|
||
"Note: you may need to restart the kernel to use updated packages.\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"%pip install --upgrade pip\n",
|
||
"%pip install --upgrade jupyter\n",
|
||
"%pip install --upgrade ipywidgets\n",
|
||
"%pip install --upgrade ipykernel"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 2,
|
||
"id": "13103c58",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Requirement already satisfied: transformers in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (4.57.3)\n",
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||
"Requirement already satisfied: pillow in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (12.0.0)\n",
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||
"Requirement already satisfied: paddleocr in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (3.3.2)\n",
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||
"Requirement already satisfied: hf_xet in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (1.2.0)\n",
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"Requirement already satisfied: paddlepaddle in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (3.2.2)\n",
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"Requirement already satisfied: jiwer in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (4.0.0)\n",
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"Requirement already satisfied: rich in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (14.2.0)\n",
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||
"Requirement already satisfied: filelock in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from transformers) (3.20.0)\n",
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||
"Requirement already satisfied: huggingface-hub<1.0,>=0.34.0 in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from transformers) (0.36.0)\n",
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"Requirement already satisfied: numpy>=1.17 in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from transformers) (2.3.5)\n",
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"Requirement already satisfied: packaging>=20.0 in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from transformers) (25.0)\n",
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||
"Requirement already satisfied: pyyaml>=5.1 in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from transformers) (6.0.2)\n",
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||
"Requirement already satisfied: regex!=2019.12.17 in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from transformers) (2025.11.3)\n",
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"Requirement already satisfied: requests in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from transformers) (2.32.5)\n",
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||
"Requirement already satisfied: tokenizers<=0.23.0,>=0.22.0 in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from transformers) (0.22.1)\n",
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||
"Requirement already satisfied: safetensors>=0.4.3 in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from transformers) (0.7.0)\n",
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||
"Requirement already satisfied: tqdm>=4.27 in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from transformers) (4.67.1)\n",
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"Requirement already satisfied: fsspec>=2023.5.0 in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from huggingface-hub<1.0,>=0.34.0->transformers) (2025.12.0)\n",
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||
"Requirement already satisfied: typing-extensions>=3.7.4.3 in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from huggingface-hub<1.0,>=0.34.0->transformers) (4.15.0)\n",
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||
"Requirement already satisfied: paddlex<3.4.0,>=3.3.0 in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from paddlex[ocr-core]<3.4.0,>=3.3.0->paddleocr) (3.3.10)\n",
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||
"Requirement already satisfied: aistudio-sdk>=0.3.5 in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from paddlex<3.4.0,>=3.3.0->paddlex[ocr-core]<3.4.0,>=3.3.0->paddleocr) (0.3.8)\n",
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||
"Requirement already satisfied: chardet in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from paddlex<3.4.0,>=3.3.0->paddlex[ocr-core]<3.4.0,>=3.3.0->paddleocr) (5.2.0)\n",
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||
"Requirement already satisfied: colorlog in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from paddlex<3.4.0,>=3.3.0->paddlex[ocr-core]<3.4.0,>=3.3.0->paddleocr) (6.10.1)\n",
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||
"Requirement already satisfied: modelscope>=1.28.0 in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from paddlex<3.4.0,>=3.3.0->paddlex[ocr-core]<3.4.0,>=3.3.0->paddleocr) (1.32.0)\n",
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||
"Requirement already satisfied: pandas>=1.3 in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from paddlex<3.4.0,>=3.3.0->paddlex[ocr-core]<3.4.0,>=3.3.0->paddleocr) (2.3.3)\n",
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||
"Requirement already satisfied: prettytable in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from paddlex<3.4.0,>=3.3.0->paddlex[ocr-core]<3.4.0,>=3.3.0->paddleocr) (3.17.0)\n",
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||
"Requirement already satisfied: py-cpuinfo in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from paddlex<3.4.0,>=3.3.0->paddlex[ocr-core]<3.4.0,>=3.3.0->paddleocr) (9.0.0)\n",
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||
"Requirement already satisfied: pydantic>=2 in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from paddlex<3.4.0,>=3.3.0->paddlex[ocr-core]<3.4.0,>=3.3.0->paddleocr) (2.12.5)\n",
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||
"Requirement already satisfied: ruamel.yaml in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from paddlex<3.4.0,>=3.3.0->paddlex[ocr-core]<3.4.0,>=3.3.0->paddleocr) (0.18.16)\n",
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||
"Requirement already satisfied: ujson in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from paddlex<3.4.0,>=3.3.0->paddlex[ocr-core]<3.4.0,>=3.3.0->paddleocr) (5.11.0)\n",
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||
"Requirement already satisfied: imagesize in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from paddlex[ocr-core]<3.4.0,>=3.3.0->paddleocr) (1.4.1)\n",
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||
"Requirement already satisfied: opencv-contrib-python==4.10.0.84 in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from paddlex[ocr-core]<3.4.0,>=3.3.0->paddleocr) (4.10.0.84)\n",
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||
"Requirement already satisfied: pyclipper in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from paddlex[ocr-core]<3.4.0,>=3.3.0->paddleocr) (1.4.0)\n",
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||
"Requirement already satisfied: pypdfium2>=4 in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from paddlex[ocr-core]<3.4.0,>=3.3.0->paddleocr) (5.1.0)\n",
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||
"Requirement already satisfied: python-bidi in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from paddlex[ocr-core]<3.4.0,>=3.3.0->paddleocr) (0.6.7)\n",
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||
"Requirement already satisfied: shapely in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from paddlex[ocr-core]<3.4.0,>=3.3.0->paddleocr) (2.1.2)\n",
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||
"Requirement already satisfied: httpx in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from paddlepaddle) (0.28.1)\n",
|
||
"Requirement already satisfied: protobuf>=3.20.2 in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from paddlepaddle) (6.33.2)\n",
|
||
"Requirement already satisfied: opt-einsum==3.3.0 in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from paddlepaddle) (3.3.0)\n",
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||
"Requirement already satisfied: networkx in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from paddlepaddle) (3.6)\n",
|
||
"Requirement already satisfied: click>=8.1.8 in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from jiwer) (8.2.1)\n",
|
||
"Requirement already satisfied: rapidfuzz>=3.9.7 in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from jiwer) (3.14.3)\n",
|
||
"Requirement already satisfied: markdown-it-py>=2.2.0 in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from rich) (4.0.0)\n",
|
||
"Requirement already satisfied: pygments<3.0.0,>=2.13.0 in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from rich) (2.19.2)\n",
|
||
"Requirement already satisfied: psutil in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from aistudio-sdk>=0.3.5->paddlex<3.4.0,>=3.3.0->paddlex[ocr-core]<3.4.0,>=3.3.0->paddleocr) (7.1.3)\n",
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||
"Requirement already satisfied: bce-python-sdk in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from aistudio-sdk>=0.3.5->paddlex<3.4.0,>=3.3.0->paddlex[ocr-core]<3.4.0,>=3.3.0->paddleocr) (0.9.55)\n",
|
||
"Requirement already satisfied: colorama in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from click>=8.1.8->jiwer) (0.4.6)\n",
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||
"Requirement already satisfied: mdurl~=0.1 in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from markdown-it-py>=2.2.0->rich) (0.1.2)\n",
|
||
"Requirement already satisfied: setuptools in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from modelscope>=1.28.0->paddlex<3.4.0,>=3.3.0->paddlex[ocr-core]<3.4.0,>=3.3.0->paddleocr) (65.5.0)\n",
|
||
"Requirement already satisfied: urllib3>=1.26 in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from modelscope>=1.28.0->paddlex<3.4.0,>=3.3.0->paddlex[ocr-core]<3.4.0,>=3.3.0->paddleocr) (2.6.0)\n",
|
||
"Requirement already satisfied: python-dateutil>=2.8.2 in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from pandas>=1.3->paddlex<3.4.0,>=3.3.0->paddlex[ocr-core]<3.4.0,>=3.3.0->paddleocr) (2.9.0.post0)\n",
|
||
"Requirement already satisfied: pytz>=2020.1 in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from pandas>=1.3->paddlex<3.4.0,>=3.3.0->paddlex[ocr-core]<3.4.0,>=3.3.0->paddleocr) (2025.2)\n",
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||
"Requirement already satisfied: tzdata>=2022.7 in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from pandas>=1.3->paddlex<3.4.0,>=3.3.0->paddlex[ocr-core]<3.4.0,>=3.3.0->paddleocr) (2025.2)\n",
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||
"Requirement already satisfied: annotated-types>=0.6.0 in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from pydantic>=2->paddlex<3.4.0,>=3.3.0->paddlex[ocr-core]<3.4.0,>=3.3.0->paddleocr) (0.7.0)\n",
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||
"Requirement already satisfied: pydantic-core==2.41.5 in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from pydantic>=2->paddlex<3.4.0,>=3.3.0->paddlex[ocr-core]<3.4.0,>=3.3.0->paddleocr) (2.41.5)\n",
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||
"Requirement already satisfied: typing-inspection>=0.4.2 in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from pydantic>=2->paddlex<3.4.0,>=3.3.0->paddlex[ocr-core]<3.4.0,>=3.3.0->paddleocr) (0.4.2)\n",
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||
"Requirement already satisfied: six>=1.5 in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from python-dateutil>=2.8.2->pandas>=1.3->paddlex<3.4.0,>=3.3.0->paddlex[ocr-core]<3.4.0,>=3.3.0->paddleocr) (1.17.0)\n",
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"Requirement already satisfied: charset_normalizer<4,>=2 in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from requests->transformers) (3.4.4)\n",
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"Requirement already satisfied: idna<4,>=2.5 in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from requests->transformers) (3.11)\n",
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"Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from requests->transformers) (2025.11.12)\n",
|
||
"Requirement already satisfied: pycryptodome>=3.8.0 in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from bce-python-sdk->aistudio-sdk>=0.3.5->paddlex<3.4.0,>=3.3.0->paddlex[ocr-core]<3.4.0,>=3.3.0->paddleocr) (3.23.0)\n",
|
||
"Requirement already satisfied: future>=0.6.0 in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from bce-python-sdk->aistudio-sdk>=0.3.5->paddlex<3.4.0,>=3.3.0->paddlex[ocr-core]<3.4.0,>=3.3.0->paddleocr) (1.0.0)\n",
|
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"Requirement already satisfied: anyio in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from httpx->paddlepaddle) (4.12.0)\n",
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||
"Requirement already satisfied: httpcore==1.* in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from httpx->paddlepaddle) (1.0.9)\n",
|
||
"Requirement already satisfied: h11>=0.16 in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from httpcore==1.*->httpx->paddlepaddle) (0.16.0)\n",
|
||
"Requirement already satisfied: wcwidth in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from prettytable->paddlex<3.4.0,>=3.3.0->paddlex[ocr-core]<3.4.0,>=3.3.0->paddleocr) (0.2.14)\n",
|
||
"Requirement already satisfied: ruamel.yaml.clib>=0.2.7 in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from ruamel.yaml->paddlex<3.4.0,>=3.3.0->paddlex[ocr-core]<3.4.0,>=3.3.0->paddleocr) (0.2.15)\n",
|
||
"Note: you may need to restart the kernel to use updated packages.\n",
|
||
"Requirement already satisfied: pandas in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (2.3.3)\n",
|
||
"Requirement already satisfied: numpy>=1.23.2 in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from pandas) (2.3.5)\n",
|
||
"Requirement already satisfied: python-dateutil>=2.8.2 in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from pandas) (2.9.0.post0)\n",
|
||
"Requirement already satisfied: pytz>=2020.1 in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from pandas) (2025.2)\n",
|
||
"Requirement already satisfied: tzdata>=2022.7 in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from pandas) (2025.2)\n",
|
||
"Requirement already satisfied: six>=1.5 in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from python-dateutil>=2.8.2->pandas) (1.17.0)\n",
|
||
"Note: you may need to restart the kernel to use updated packages.\n",
|
||
"Requirement already satisfied: matplotlib in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (3.10.7)\n",
|
||
"Requirement already satisfied: contourpy>=1.0.1 in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from matplotlib) (1.3.3)\n",
|
||
"Requirement already satisfied: cycler>=0.10 in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from matplotlib) (0.12.1)\n",
|
||
"Requirement already satisfied: fonttools>=4.22.0 in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from matplotlib) (4.61.0)\n",
|
||
"Requirement already satisfied: kiwisolver>=1.3.1 in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from matplotlib) (1.4.9)\n",
|
||
"Requirement already satisfied: numpy>=1.23 in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from matplotlib) (2.3.5)\n",
|
||
"Requirement already satisfied: packaging>=20.0 in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from matplotlib) (25.0)\n",
|
||
"Requirement already satisfied: pillow>=8 in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from matplotlib) (12.0.0)\n",
|
||
"Requirement already satisfied: pyparsing>=3 in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from matplotlib) (3.2.5)\n",
|
||
"Requirement already satisfied: python-dateutil>=2.7 in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from matplotlib) (2.9.0.post0)\n",
|
||
"Requirement already satisfied: six>=1.5 in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from python-dateutil>=2.7->matplotlib) (1.17.0)\n",
|
||
"Note: you may need to restart the kernel to use updated packages.\n",
|
||
"Requirement already satisfied: seaborn in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (0.13.2)\n",
|
||
"Requirement already satisfied: numpy!=1.24.0,>=1.20 in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from seaborn) (2.3.5)\n",
|
||
"Requirement already satisfied: pandas>=1.2 in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from seaborn) (2.3.3)\n",
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||
"Requirement already satisfied: matplotlib!=3.6.1,>=3.4 in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from seaborn) (3.10.7)\n",
|
||
"Requirement already satisfied: contourpy>=1.0.1 in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.3.3)\n",
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||
"Requirement already satisfied: cycler>=0.10 in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (0.12.1)\n",
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"Requirement already satisfied: fonttools>=4.22.0 in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (4.61.0)\n",
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||
"Requirement already satisfied: kiwisolver>=1.3.1 in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.4.9)\n",
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||
"Requirement already satisfied: packaging>=20.0 in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (25.0)\n",
|
||
"Requirement already satisfied: pillow>=8 in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (12.0.0)\n",
|
||
"Requirement already satisfied: pyparsing>=3 in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (3.2.5)\n",
|
||
"Requirement already satisfied: python-dateutil>=2.7 in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (2.9.0.post0)\n",
|
||
"Requirement already satisfied: pytz>=2020.1 in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from pandas>=1.2->seaborn) (2025.2)\n",
|
||
"Requirement already satisfied: tzdata>=2022.7 in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from pandas>=1.2->seaborn) (2025.2)\n",
|
||
"Requirement already satisfied: six>=1.5 in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from python-dateutil>=2.7->matplotlib!=3.6.1,>=3.4->seaborn) (1.17.0)\n",
|
||
"Note: you may need to restart the kernel to use updated packages.\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# Install necessary packages\n",
|
||
"%pip install transformers pillow paddleocr hf_xet paddlepaddle jiwer rich\n",
|
||
"\n",
|
||
"\n",
|
||
"\n",
|
||
"# Data analysis and visualization\n",
|
||
"%pip install pandas\n",
|
||
"%pip install matplotlib\n",
|
||
"%pip install seaborn"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 1,
|
||
"id": "ae33632a",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# Imports\n",
|
||
"import os, json\n",
|
||
"import numpy as np\n",
|
||
"import pandas as pd\n",
|
||
"import matplotlib.pyplot as plt\n",
|
||
"\n",
|
||
"import re\n",
|
||
"from datetime import datetime\n",
|
||
"\n",
|
||
"from rich.console import Console\n",
|
||
"import colorama\n",
|
||
"\n",
|
||
"colorama.just_fix_windows_console()\n",
|
||
"# Tell Ray Tune to use a Jupyter-compatible console\n",
|
||
"console = Console(force_jupyter=True)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "0e00f1b0",
|
||
"metadata": {},
|
||
"source": [
|
||
"## 1 Configuration"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 16,
|
||
"id": "8bfa3329",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"PDF_FOLDER = './dataset' # Folder containing PDF files\n",
|
||
"OUTPUT_FOLDER = 'results'\n",
|
||
"os.makedirs(OUTPUT_FOLDER, exist_ok=True)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 17,
|
||
"id": "8bd4ca23",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"c:\\Users\\sji\\Desktop\\MastersThesis\\src\\dataset\n",
|
||
"c:\\Users\\sji\\Desktop\\MastersThesis\\src\\paddle_ocr_tuning.py\n",
|
||
"c:\\Users\\sji\\Desktop\\MastersThesis\\src\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"PDF_FOLDER_ABS = os.path.abspath(PDF_FOLDER) # ./instructions -> C:\\...\\instructions\n",
|
||
"SCRIPT_ABS = os.path.abspath(\"paddle_ocr_tuning.py\") # paddle_ocr_tuning.py -> C:\\...\\paddle_ocr_tuning.py\n",
|
||
"SCRIPT_DIR = os.path.dirname(SCRIPT_ABS)\n",
|
||
"\n",
|
||
"print(PDF_FOLDER_ABS)\n",
|
||
"print(SCRIPT_ABS)\n",
|
||
"print(SCRIPT_DIR)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 4,
|
||
"id": "9c658b58",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"c:\\Users\\Sergio\\Desktop\\MastersThesis\\.venv\\Lib\\site-packages\\paddle\\utils\\cpp_extension\\extension_utils.py:718: UserWarning: No ccache found. Please be aware that recompiling all source files may be required. You can download and install ccache from: https://github.com/ccache/ccache/blob/master/doc/INSTALL.md\n",
|
||
" warnings.warn(warning_message)\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Paddle version: 3.2.2\n",
|
||
"GPU available: False\n",
|
||
"GPU count: 0\n",
|
||
"Current device: cpu\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"import paddle\n",
|
||
"\n",
|
||
"print(\"Paddle version:\", paddle.__version__)\n",
|
||
"print(\"GPU available:\", paddle.device.is_compiled_with_cuda())\n",
|
||
"print(\"GPU count:\", paddle.device.cuda.device_count())\n",
|
||
"print(\"Current device:\", paddle.device.get_device())"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 7,
|
||
"id": "243849b9",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\u001b[32mCreating model: ('PP-LCNet_x1_0_doc_ori', None)\u001b[0m\n",
|
||
"\u001b[32mModel files already exist. Using cached files. To redownload, please delete the directory manually: `C:\\Users\\Sergio\\.paddlex\\official_models\\PP-LCNet_x1_0_doc_ori`.\u001b[0m\n",
|
||
"\u001b[32mCreating model: ('UVDoc', None)\u001b[0m\n",
|
||
"\u001b[32mModel files already exist. Using cached files. To redownload, please delete the directory manually: `C:\\Users\\Sergio\\.paddlex\\official_models\\UVDoc`.\u001b[0m\n",
|
||
"\u001b[32mCreating model: ('PP-LCNet_x1_0_textline_ori', None)\u001b[0m\n",
|
||
"\u001b[32mModel files already exist. Using cached files. To redownload, please delete the directory manually: `C:\\Users\\Sergio\\.paddlex\\official_models\\PP-LCNet_x1_0_textline_ori`.\u001b[0m\n",
|
||
"\u001b[32mCreating model: ('PP-OCRv5_server_det', None)\u001b[0m\n",
|
||
"\u001b[32mModel files already exist. Using cached files. To redownload, please delete the directory manually: `C:\\Users\\Sergio\\.paddlex\\official_models\\PP-OCRv5_server_det`.\u001b[0m\n",
|
||
"\u001b[32mCreating model: ('PP-OCRv5_server_rec', None)\u001b[0m\n",
|
||
"\u001b[32mModel files already exist. Using cached files. To redownload, please delete the directory manually: `C:\\Users\\Sergio\\.paddlex\\official_models\\PP-OCRv5_server_rec`.\u001b[0m\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# 3. PaddleOCR \n",
|
||
"# https://www.paddleocr.ai/v3.0.0/en/version3.x/pipeline_usage/OCR.html?utm_source=chatgpt.com#21-command-line\n",
|
||
"from paddleocr import PaddleOCR\n",
|
||
"\n",
|
||
"# Initialize with better settings for Spanish/Latin text\n",
|
||
"# https://www.paddleocr.ai/main/en/version3.x/algorithm/PP-OCRv5/PP-OCRv5_multi_languages.html?utm_source=chatgpt.com#5-models-and-their-supported-languages\n",
|
||
"paddleocr_model = PaddleOCR(\n",
|
||
" text_detection_model_name=\"PP-OCRv5_server_det\",\n",
|
||
" text_recognition_model_name=\"PP-OCRv5_server_rec\"\n",
|
||
")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 8,
|
||
"id": "329da34a",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"3.3.2\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"import paddleocr\n",
|
||
"\n",
|
||
"print(paddleocr.__version__)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 9,
|
||
"id": "b1541bb6",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"c:\\Users\\Sergio\\Desktop\\MastersThesis\\.venv\\Lib\\site-packages\\paddleocr\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# 1) Locate the installed PaddleOCR package\n",
|
||
"pkg_dir = os.path.dirname(paddleocr.__file__)\n",
|
||
"print(pkg_dir)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "84c999e2",
|
||
"metadata": {},
|
||
"source": [
|
||
"## 2 Helper Functions"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 10,
|
||
"id": "9596c7df",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"from typing import List, Optional\n",
|
||
"from paddle_ocr_tuning import evaluate_text, assemble_from_paddle_result\n",
|
||
"from dataset_manager import ImageTextDataset"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 11,
|
||
"id": "b7c1bbf8",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"from PIL import Image\n",
|
||
"\n",
|
||
"def show_page(img: Image.Image, scale: float = 1):\n",
|
||
" \"\"\"\n",
|
||
" Displays a smaller version of the image with text as a footer.\n",
|
||
" \"\"\"\n",
|
||
" # Compute plot size based on image dimensions (but without resizing the image)\n",
|
||
" w, h = img.size\n",
|
||
" figsize = (w * scale / 100, h * scale / 100) # convert pixels to inches approx\n",
|
||
"\n",
|
||
" fig, ax = plt.subplots(figsize=figsize)\n",
|
||
" ax.imshow(img)\n",
|
||
" ax.axis(\"off\")\n",
|
||
"\n",
|
||
"\n",
|
||
" # Add OCR text below the image (footer)\n",
|
||
" # plt.figtext(0.5, 0.02, text.strip(), wrap=True, ha='center', va='bottom', fontsize=10)\n",
|
||
" plt.tight_layout()\n",
|
||
" plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 12,
|
||
"id": "b9d3fe25",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 372.15x526.2 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Índice\n",
|
||
"1. Indicaciones generales 3\n",
|
||
"1.1. Línea de discurso 3\n",
|
||
"1.2. Estructura general y extensión del TFE 4\n",
|
||
"1.3. Formatos y plantilla de trabajo 5\n",
|
||
"1.4. Estética y estilo de redacción 7\n",
|
||
"1.5. Normativa de citas 8\n",
|
||
"2. Estructura del documento 9\n",
|
||
"2.1. Resumen 10\n",
|
||
"2.2. Organización del trabajo en grupo 11\n",
|
||
"2.3. Introducción 11\n",
|
||
"2.4. Contexto y estado del arte 13\n",
|
||
"2.5. Objetivos concretos y metodología de trabajo 14\n",
|
||
"2.6. Desarrollo específico de la contribución 17\n",
|
||
"2.7. Conclusiones y trabajo futuro 20\n",
|
||
"2.8. Referencias bibliográficas 21\n",
|
||
"© Universidad Internacional de La Rioja (UNIR)\n",
|
||
"2.8.1. Herramientas para buscar bibliografía 22\n",
|
||
"2.9. Anexos 23\n",
|
||
"2.10. Índice de acrónimos 24\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"#test\n",
|
||
"dataset = ImageTextDataset(PDF_FOLDER_ABS)\n",
|
||
"img, txt = dataset[1]\n",
|
||
"show_page(img, 0.15)\n",
|
||
"print(txt)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 13,
|
||
"id": "dcd27755",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 372.15x526.2 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Superior e inferior: 2,5 cm.\n",
|
||
"Formato de párrafo en texto principal (estilo de la plantilla “Normal”):\n",
|
||
" Calibri 12, justificado, interlineado 1,5, espacio entre párrafos 6 puntos\n",
|
||
"anterior y 6 puntos posterior, sin sangría.\n",
|
||
"Títulos:\n",
|
||
" Primer nivel (estilo de la plantilla “Título 1”): Calibri Light 18, azul, justificado,\n",
|
||
"interlineado 1,5, espacio entre párrafos 6 puntos anterior y 6 puntos\n",
|
||
"posterior, sin sangría.\n",
|
||
" Segundo nivel (estilo de la plantilla “Título 2”): Calibri Light 14, azul,\n",
|
||
"justificado, interlineado 1,5, espacio entre párrafos 6 puntos anterior y 6\n",
|
||
"puntos posterior, sin sangría.\n",
|
||
" Tercer nivel (estilo de la plantilla “Título 3”: Calibri Light 12, justificado,\n",
|
||
"interlineado 1,5, espacio entre párrafos 6 puntos anterior y 6 puntos\n",
|
||
"posterior, sin sangría.\n",
|
||
"Notas al pie:\n",
|
||
" Calibri 10, justificado, interlineado sencillo, espacio entre párrafos 0 puntos\n",
|
||
"anterior y 0 puntos posterior, sin sangría.\n",
|
||
"Tablas y figuras:\n",
|
||
" Título en la parte superior de la tabla o figura.\n",
|
||
" Numeración tabla o figura (Tabla 1/ Figura1): Calibri 12, negrita, justificado.\n",
|
||
" Nombre tabla o figura: Calibri 12, cursiva, justificado.\n",
|
||
" Cuerpo: la tipografía de las tablas o figuras se pueden reducir hasta los 9\n",
|
||
"puntos si estas contienen mucha información. Si la tabla o figura es muy\n",
|
||
"grande, también se puede colocar en apaisado dentro de la hoja.\n",
|
||
" Fuente de la tabla o figura en la parte inferior. Calibri 9,5, centrado.\n",
|
||
"Encabezado y pie de página:\n",
|
||
" Todas las páginas llevarán un encabezado con el nombre completo del\n",
|
||
"estudiante y el título del TFE.\n",
|
||
"© Universidad Internacional de La Rioja (UNIR)\n",
|
||
" Todas las páginas llevarán también un pie de página con el número de página.\n",
|
||
"Instrucciones para la redacción y elaboración del TFE\n",
|
||
"6\n",
|
||
"Máster Universitario en Inteligencia Artificial\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"dataset = ImageTextDataset(PDF_FOLDER_ABS)\n",
|
||
"img, txt = dataset[5]\n",
|
||
"show_page(img, 0.15)\n",
|
||
"print(txt)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "e42cae29",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Run AI OCR Benchmark"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 14,
|
||
"id": "9b55c154",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 372.15x526.2 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"ref: \n",
|
||
"Superior e inferior: 2,5 cm.\n",
|
||
"Formato de párrafo en texto principal (estilo de la plantilla “Normal”):\n",
|
||
" Calibri 12, justificado, interlineado 1,5, espacio entre párrafos 6 puntos\n",
|
||
"anterior y 6 puntos posterior, sin sangría.\n",
|
||
"Títulos:\n",
|
||
" Primer nivel (estilo de la plantilla “Título 1”): Calibri Light 18, azul, justificado,\n",
|
||
"interlineado 1,5, espacio entre párrafos 6 puntos anterior y 6 puntos\n",
|
||
"posterior, sin sangría.\n",
|
||
" Segundo nivel (estilo de la plantilla “Título 2”): Calibri Light 14, azul,\n",
|
||
"justificado, interlineado 1,5, espacio entre párrafos 6 puntos anterior y 6\n",
|
||
"puntos posterior, sin sangría.\n",
|
||
" Tercer nivel (estilo de la plantilla “Título 3”: Calibri Light 12, justificado,\n",
|
||
"interlineado 1,5, espacio entre párrafos 6 puntos anterior y 6 puntos\n",
|
||
"posterior, sin sangría.\n",
|
||
"Notas al pie:\n",
|
||
" Calibri 10, justificado, interlineado sencillo, espacio entre párrafos 0 puntos\n",
|
||
"anterior y 0 puntos posterior, sin sangría.\n",
|
||
"Tablas y figuras:\n",
|
||
" Título en la parte superior de la tabla o figura.\n",
|
||
" Numeración tabla o figura (Tabla 1/ Figura1): Calibri 12, negrita, justificado.\n",
|
||
" Nombre tabla o figura: Calibri 12, cursiva, justificado.\n",
|
||
" Cuerpo: la tipografía de las tablas o figuras se pueden reducir hasta los 9\n",
|
||
"puntos si estas contienen mucha información. Si la tabla o figura es muy\n",
|
||
"grande, también se puede colocar en apaisado dentro de la hoja.\n",
|
||
" Fuente de la tabla o figura en la parte inferior. Calibri 9,5, centrado.\n",
|
||
"Encabezado y pie de página:\n",
|
||
" Todas las páginas llevarán un encabezado con el nombre completo del\n",
|
||
"estudiante y el título del TFE.\n",
|
||
"© Universidad Internacional de La Rioja (UNIR)\n",
|
||
" Todas las páginas llevarán también un pie de página con el número de página.\n",
|
||
"Instrucciones para la redacción y elaboración del TFE\n",
|
||
"6\n",
|
||
"Máster Universitario en Inteligencia Artificial\n",
|
||
"paddle_text: \n",
|
||
"Superior e inferior: 2,5 cm.\n",
|
||
"Formato de párrafo en texto principal (estilo de la plantilla “Normal\"):\n",
|
||
"Calibri 12, justificado, interlineado 1,5, espacio entre párrafos 6 puntos\n",
|
||
"anterior y 6 puntos posterior, sin sangría.\n",
|
||
"Títulos:\n",
|
||
"Primer nivel (estilo de la plantillaTítulo 1\"): Calibri Light 18, azul, justificado,\n",
|
||
"interlineado 1,5,espacio entre párrafos 6 puntos anterior y 6 puntos\n",
|
||
"posterior, sin sangría.\n",
|
||
"Segundo nivel (estilo de la plantilla Titulo 2\"): Calibri Light 14, azul,\n",
|
||
"justificado, interlineado 1,5, espacio entre párrafos 6 puntos anterior y 6\n",
|
||
"puntos posterior, sin sangría.\n",
|
||
"Tercer nivel (estilo de la plantilla Título 3\": Calibri Light 12, justificado,\n",
|
||
"interlineado 1,5,espacio entre párrafos 6 puntos anterior y 6 puntos\n",
|
||
"posterior, sin sangría.\n",
|
||
"Notas al pie:\n",
|
||
"Calibri 10, justificado, interlineado sencillo, espacio entre párrafos O puntos\n",
|
||
"anterior y O puntos posterior, sin sangra.\n",
|
||
"Tablas y figuras:\n",
|
||
"Título en la parte superior de la tabla o figura.\n",
|
||
"Numeración tabla o figura (Tabla 1/ Figura1): Calibri 12, negrita, justificado.\n",
|
||
"Nombre tabla o figura: Calibri 12, cursiva, justificado.\n",
|
||
"Cuerpo: la tipografía de las tablas o figuras se pueden reducir hasta los 9\n",
|
||
"puntos si estas contienen mucha información. Si la tabla o figura es muy\n",
|
||
"grande, también se puede colocar en apaisado dentro de la hoja.\n",
|
||
"Fuente de la tabla o figura en la parte inferior. Calibri 9,5, centrado.\n",
|
||
"Encabezado y pie de página:\n",
|
||
"Todas las páginas llevarán un encabezado con el nombre completo del\n",
|
||
"estudiante y el título del TFE.\n",
|
||
"© Universidad Internacional de La Rioja (UNiR)\n",
|
||
"Todas las páginas llevarán también un pie de página con el número de página.\n",
|
||
"Instrucciones para la redacción y elaboración del TFE\n",
|
||
"Máster Universitario en Inteligencia Artificial 9\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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WAX+TLr70mOf9ebbB/hHvZ8/jQGXN+XzZ4/poytC+dcvUo13zE7w0a6LesafZVf1r368iSZftotLmNpmbA9q1atEsibpZ3/VR8PVCdcdEtd9bUPB1QrPeoy4oKCj4KqAQdUFBQUGdoxB1QUFBQZ2jEPUBEQLnw2jCLIn9qv0wube6mfYrV+6CguaHQtQphYSW5DcD8VK7bdq46ZANSE3mntvae2fIdMmsqbaOFNJ7642ZIbTx93nclCPNZUuWpPvuvD1t3rT5qNOgtjtQ+Sj75s15N2TFDHXcE/3evLmhanSOz+e9+07cu9sMbzwqKDgiNOu7Pii6Pv5obQgICEoo6yjbKOHmvfNOqLZ27+oTQgk+G5RZxAqMlbZv2546du6cFr33Xog5AOHwKSBYmfLIw+n0CWeEZ4LbxYgwNm/cGJ4bVHqdOndOaz9aE/4GzuenYGGg4KLeW7NqdVq/bm268vpvhomRNIg8GDNRF5L9kmETAVBKkn0TFxAagHrwbfC9fHfu2hkqMCpFt7A9ct894Z9AZbLigw9CWEJAsmnDhlCpUdfJEyrV4N4QJ8hHuxFX/Ozv/y7d9DvfC+HM6lWrIg1t5+6Lpx59OM2b806IfgiESM35OyxaMD997w//JN3xq1/EsXxNiB0KCgoOjGZN1DNefjmiaf4F/QcOSg/efWcQWVgqbt8WHh1bNm0OlR0lHnKlopr12oxQZjHA4f3BlOi7P/zDIMD//p/+OpR7H69dE5H2M088FsSFrMiWyZgZ+jCyoRZDghRiFG5Dhg0L4x8LB8JGoJR70n9/4cIwX0KwlFrKR/5M/cf5jgIOsf7RP/+/hQvZL/7xR2F0M/KkMemV558NFRlV3r/+X/73yr1t3txQqd13+2/TmlWrwrzn2m/dmG77xc/CHMkVhnKLfAcNHRr///0/++eh4uQ74hgKOIo5C4XjpPH7f/bPonzI/sSTTo5Fa9WHK0OA8ju//4dpyqMPhTSYTJxq07EFBQUHR7Pe+li8YH666Iorw8CIlwHjoDBW2rkrok8kOPqUU8IwiYkR4hJVkwdfdvW1IWvmoIaMyIMRoYgaoZGTcz6TrkhT9EvG/f0/+bP4jKkOqfQV114XPhCiyuXLlsV2ibROPm1smnDm2bEgZC9h/g8Wge//8Z+GLJpBkryZQvF5cN6u3bvCVQ75Iu+N69cHaX73h38QfhUWCaTJce2Mc8+L7Ydv/94PQrK7YN68fYT6R0G+N9x8S0TxN9x0S9Rh06aNcQVga4cEeOCgwWnyxZeE2VOkMerEtPC99xral4mQBWI/heDeyufC1QfyJ2ePj8v2R0HBAdGsiRoJvfbyS+H30alTp/Dp4HtQWYvuDd8DPhYsIkXatkdGjBodBjRIm7cB45l2bdvFFkJsJaSU7r71N2HizpeDIQ+/Br4Ta9d+lO789S+D5ESkLz33bJr25JQguL7HHx/bJGtWfRj+HAiTfwWy44onmifksDggZaY4FgxeCKwkle+UsePD4KZf/wFxPB+GkSedFJ9V5jdtwiwJpOVzxzx8793hOyGil6bv+I+op4XBa+s2bcPNbsXyZVFPnzFz4n/Sf8CAfWnMjzRy+hdednksEN279wgfjt/89Cdx/PkXXZzatmufLrj08mhr7noWkIKCgqbRrCXkm7ZuS+8vWhhRH3c5+86i1N59+4YxjAiZKRMyQ4iiYkYz27Zsjf1g3hKiYOdzcmN64zjnIXDWlPx3t23ZEiZN//0//U367g/+IIgVeS1auCBok4kNK0xbJyJoUab0Rfj+r0z2nrt07RZ5Hj9gYNhl2k8+YeSo2N+WD8Mjkbn62efmM22xsMfNlIhHsP1kC4EFgY2kBQdJM8Th0WzbRPorli9PvY47LiL2Xr37RISujZA145/uPXtFWbTZcX37xAMYpMEa1naO8xj/2J4R0dtiUi5ltOev3Y7r0zvMrOTJrMhCUlBwMLRrphLyZk3UX6TXBztVPtN+YCwS54KCo0O7QtTNB8WUqaDgq4l2zZSom/UedUFBQcFXAYWoCwoKCuoczfY+atvTu4sfdUHBVwp7WjavLY/U3Il64cYd6aNtzfepxgUFX0X06dAm9Wrf/O4OarZ3fRQUFHx10aKZ/ZjYLCPq5tbJBQUFX22UHxMLCgoK6hyFqAsKCgrqHIWoCwoKCuochagLCgoK6hyFqAsKCgrqHIWoCwoKCuochagLCgoK6hyFqAsKCgrqHIWoCwoKCuochagLCgoK6hyFqAsKCgrqHIWoCwoKCuochagLCgoK6hyFqAsKCgrqHIWoCwoKCuochagLCgoK6hyFqAsKCgrqHIWoCwoKCuochagLCgoK6hyFqAsKCgrqHIWoCwoKCuochagLCgoK6hyFqAsKCgrqHIWoCwoKCuochagLCgoK6hyFqAsKCgrqHIWoCwoKCuochahTSnv3+tub6gHKsX7H7rRj954Dfl8vZW1cJn/bdu9J23btOao0tuzak3bt+XzqJv2txyj9pvpg++49acOO3XXXNwVfDzR7ot6zd296fc2WdN+i9fG+KZh8H2zekf7xndXxejCs2bor/XTOmjjW30PvN52uNJFx7cT2fsWWXel/eG5pWrNtV3y2e8/etHNPRQy79+5NP353TXr7423py0Que67Xii070395c1Xavntv+vW8temncz868jRTSv/65Q+iLz4v/L9eXZ6eX7npM6Whzr+YuzbNXLO14TN99Devf5geX7rhGJSyoODTaNZEjQD/9q1V6V++uCz9t9mr0gGC2LRrb0r/cebK9P9+bWWavfbgJPnuum3pv765Ku3ck9LuvdXEbhx1wrbde9P/8PzStHJLRcgZr67anL43smfq37FN/P/p5RvTf571YbxvkVJq36plat2iRZNpHuizI8GBzq/9XDP921eWN7RFyxYtUsfWLZNifbx9d1q3fddhl6fh+5TSB5t3ps010fhhn3uANq79DCyCm3RMo2MP1X6NP2vfukVq07LqA/hw687UoXXLdN2QbqnFvr45nPIXFBwuWqdmDBHqqT07pNN7dQwibgom2bTlGyNaPKtPp9gmOTj2pgGd2qY/HXNcar1vMksDqd27aF1M6O+P6pmeXLYxTVm2MfVqtypdM6RbOrdfp/Tqqi3ptTVbgvRG92gX5/9q3to0f922+Oz7o3qlLm1bpQ6tW+yXZttWLdJ3RvRIPdu1TvctWpfe/XhbGtC5TRB+93ZVFzvesSf37JBGdWuXduzZm2577+P0jaHdU7d2rRra49nlm9LTH2xMPdq1Sr8zsmfq26F1WrJpR/rtex/HtsHvjuyZ5q7blh5buiG2Oa4c1DWd379z6tKmZWrViKQ+2rY73b7g4zj/1J7t0zdP6JE6tWm53zEvr9qcHlq8Pg3v1i62JvLn89dvT3cvXJe279mbvjG0Wzq9V4cGErT4qae6Tf1gY/rjMcdF/ncsWJcWb9we/XT1kG6pdYsUka9jB3Vumzbv2t2wQE9ZuiG99OHm1L5Vi/TdkT3TCV3aRps8uHh9mrlmSxrTo0O6aVj3OPbOhR+n99ZvT+f26xz17dLmkz5YvHFHumPBx7FAPfnBxnTFoK7RjvcuXJeO79QmPbFsYxrTo326eVj31LZVs46LCj4DmvXIEZ1eOrBrkOCBsGHnnvSP76xJ/7dT+wQhHg5Wb92Z7l+8Lghi6aYdaevuvelfvfxBOrln+5i09mL7d2oTpD20a9vUq33r9OZHW9NfTV+ezu/XOQj3L15YFotCvw6tg0iHd0XcKf12/togjaWbd6Y/f25JGtKlbRrYqU16ceXmtGjj9rRx554gqVc+3JJ+Ouej/aK5hRts36yJ98jWIpCr7rjnlm9K/2nWh+niAV2SrVxltkD9+1dXpB5tW6Wz+3ZKG3fuTn06tEmdWrdMgzq1jcjfNo0tGaSWgWD/4gVXDDvTZQO7pPsXr4+rl9ryLNiwI/3F88vSST3ap1Vbd6U39m17eP8/Pr80DevaNo3t1SGueJZv2dlwnmz+ac5H6a9fXxkED//PV1ek5Vt2pKsGd0s/m/tRenTJ+rRs8870z59bEuls2rk72gT8BuDK55KBXYL8/9305XH185v5a9Ov569Nk/t3jsXF+X/9xofp5Q83p8n9u6SZq7ekDTt3p7sWfhyLoYXoj6cticXHOf/pjQ/T/YvWxR79f3x9ZWwDTTyuY/r/zV6dnl3x2bZcCpo3mnVEfSggld/MW5vGHdcxyKT289pL3MZAxHPXbU94fUS3dql3+9apbcsWQbDfG9UzHd+xTerToXU6rl2rdPnArmlol7bp/5i5MvXt2DrIrlf7Vun9jTsiwpvQu2NFvoO7xvZAxosrN6VhXdul3xvVM7ZE8ne927dJ72/cnk7o2jbNW7+94XjlvW5ot/QHz7yf1m7fnR5ZsiFdOqDLfovUPYvWRZrKLxq89b21ad2O3XGMCNeiJl1ci6AnH985TezTKc1f/+ntIOVfuGF7+rvJg1PXNi1T5zat0r96aVn6l6f1aYhGX1i5KSJlkTuifH4fmb2yanNsDcV2yj5ifmfttrhSqcW/OK1PumpQ1/Th1l1x1fM/j+0bP+hpz2nLN8UVwAld2qXvn9grGgjhQq92rdIfjO6VFqzfngZ1bhOk7urA7xT/wym9Y2G5fFDX2CZxdfGzC4ekE7u3S5cP6rJf/tNXbY5I/s/G9I6+tmBZkCwAFuF/N75fLKQi9LfXbov2Kyg4GjTriPpQsF/6q3kfRfT0P724LCKqf3x3dZDwwTCkS7v0r8b2jb+Te7RP7Vq1SP990qC0Y/fe9N0nF6XnV1aEUYv1O/bE936Ycxn9P53WJ/XucOB1dOOOPUESLfaRsNepyzelP572fqS/fPPOT+2NIuEBndqkZz7YGER27dD991RFmqJBZUDW//PpfVP3tq3Sfzjz+Ng6+MOp76e7Fqzbb8E4YNvt3BP7uO1atog8OrdpGXv9tTddIEJbOUpglyhfsViYLFj271du3RVbRa5GGg/cnu1aRdqifsQuwlX2k3t0SLcM7xHpy7flvvTb7dt6WLppZ/r+U4vTQ0vWB1lXvyVUC2y0aYsWse+O6P11qvmsdnkWXVuA8nZ117at4u4PzW4bSH7O0/+2QwoKjhbNOqLOt4T5M5Fc1ndt0SqiyBmrNqcLB3QJgt2yq/pB6J2Pt8XWhMhX1Gp/clL/zjGBa+EuAKTnRz/cYK/ZEf9qXN+0yz7wik0RKQNS7t9pbzqtV/u43L55eI+Y2Ou2747thTYtW0ZaFg1bNRkju7dLv5z3UfxAht9sDTy1bEM6t2+n9D+e0jv9f99alRZt2H9BsWf7rWHdY3tDRDxy37ZBhuh2wYbt6bsjekSdog4tWwTp/tnJxwURPbFsQ6TRqmWL9NG2XQ37yo0hIteub67dGr8DPLlsQ5CtH+IAgclfFP/R9t2xsOQF0B66Ol09pGtcjWgje+YHAsLu17FNOrVXh3R+v07RVsq/c+/e9I/vromtFGm8tXZruiX1iFfl/9dj+6XnVmyKfWT1tL30zPJN6bR97dCjXeu4uhH5+6HQ3Ta1V1bKP2/9tui3Xu1apynLNqSz+nZqIO6CgmOFZk3UorC/nL48zVu3Pfai7Tf+1YR+Ec3ZV/Qj2YTenRp+wEJkZ/bpFBPYJfHD729I5/VD1J+k6Ycm+6E/eHpx3AVxYvf2EZn++1eXp227qlvt/rcz+8ePWH54sg/8+6N7peuHdk+vrd4SkZ7LZlsM/+vE/mlC7w7p799Znf502pL0n88dGATYqU2r2LtVvu8/vTi2CHIa/8uMFWnuuiVBiI23CpDjRcd3ib3wPxx9XGzH1H7nx8p/O/2D9L2nFsdicVqvjukvTu2d/subH8Zes/b6v5/eN0j0+iHd0n+etSq2V/zoNrhz26gvYtu5p2Vs7fw/xvZN/3768qiPRed/P/P4/S7hzuvXKZ3UvV363lOLYuE4s0/HOG7scR3TDUO7R521Z5e2LdN/PGtA6rVvoVLqgZ3bpg77/i9q1m//ZdaH6R/f8VmL9Ben9U4TjusY++q/9/TiIPKJvTtGxKxPfj63RVwhdGvbKghXnWzL/M8vLwtitjD8zdkD0l+O65f+t9dWxhaYbQzlUFaRtPa5eViPKKdF1FXH75/YK6LpIZ3bNuz/H9e+9X6LbEHBkaLF3mZ871Al0KjuT669/c0bWwDVLWct9jvW5bzoa+e+y2KEW7t9gNBFmblRTVrHuDwXsZvg/g8uue2pIhrpuvwWxYLoNf92KTKVH1LJZfCd40X//o+AQDRpbxvBqVfeesiwNfDNxxak2y47IQ3tsn9EXd2rXZUBh3fdd1kv7w079sTecm4T9XSc9kLqytWhVdUu4A4H6dl+UPeubas6Nr59zXbI+n1tAG32bTG4YU+e2hhRW1Qa94XPRMb5s6279sadHcqY995zffzfofpD2ynTpl17Yv/cMblP4vPYkqnyzO3vB2Ht74LAMcaAU9RWH0qjW02fKYvFUl2yeKnc9VFwtGjWRN0c8diS9fFD4n89d2ADyRUUFNQ3ClE3M+Qfu0TBB7tzpaCgoH5QiLqgoKCgzlF+4SgoKCiocxSiLigoKKhzFKIuKCgoqHMUoi4oKCiocxSiLigoKKhzFKIuKCgoqHMUoi4oKCiocxSiLigoKKhzFKIuKCgoqHMUoi4oKCiocxSiLigoKKhzFKIuKCgoqHMUoi4oKCiocxSiLigoKKhzFKIuKCgoqHMUoi4oKCiocxSiLigoKKhzFKIuKCgoqHMUoi4oKCiocxSiPgQ8pL08qL2goODLRLMm6o8//ji98cbrafbst9KOHTs+9f3GjRvTPffck1atWnXItNatW5fefPPNz1ymXbt2pT179uz32datW9Orr04/JguGNN58c1aU1/ulS5em119/Pf7ef//9NH/+/Hi/YsWKtGzZsmgfnzUu07GANNX3y4QybN++7bDa1hi577770rZt244qr927d0caX8bCv3Pnzvg7GJRrzZo16bXXXou50VQ5D9Vnh9Onjnn55Ze/9L7/KqFZE/XMma+ld9+dk3bt2h2DEiGvWbO6IYpGWCZl27Zt0/bt2+M90ly/fn0Q2YYNG+K41atXp8WLF6c5c96N45YsWRLHeL927dq0fPkHQfrLly/fRwzVMVu2bGk45oMPPoiB++STT6ZXXnkl8kKUvvP+rbdmN5TbhFu2bGl8Z+Irh3Q3bFgfrytXrowy5YnmVf5IWR5z586LczJ5WGDUVVoWJvlJ5+GHH4oJ65gMZf7oo4+i/jlP751bm68J/+GHH8Yx2jW/1y7aQXm02R133JE2b94c6WoT7ZHbXxnl7Vjpr1v3cbSJYzK8X7DgvUjTseqoLTdt2hRlWbBgQRCxNlu4cEFD+ZRJ/nPnzk3/8T/+x4Z2fu+99+K73HbKrK2lK68pU56I49RP2sql/M5Tfvl6ry20tXq///7iaMfnn38+/ehHP0pbtmyOulkA1Vteyu6c3F6ZVKtyL2zoa3lqb8dLW7mk7TP9on1zuvJXR+nddded6e67724Yb/PmzYty15KxNH/zm9+kXbt2xjH+r73Xr68WdX/a67777o16Okba8rLI53a45567G8ZJHnN5HDpGmjNmvBqfa8c8lnJ75/GsLubb2rUfNcyd5orWqRlDxxtwBqTBboCdeeaZqWfPXvH9hx+ujMGCOPv375927Ngeg+vFF19MJ5xwQhDCNddcmx599JHUtm271KFD+5isixcvSnPmzE3nnXdeevrpp1KHDh1Ty5YtIr+LL74kSLF79+4xQMeOHZueeeaZ1K1b13T66WNjUnfs2DEdd9xxMcnefvvtdNNNNzWU2WB/9NFH07ZtW2PS3HjjTenVV19NN998c7rrrrvT6aefHp9v3rwpXXnlVWnQoEExOX7729+mvn37ppYtP1mbW7RokYYOHRqTwORUJ2V0TJs2baKuGzZsTAMHtopjYdq0qVFvpHHhhRcFcWmbfv36xwSUb1Xvp9PEiWfsi9BmRBufddbZ6YEHHkijRo2Ktj3ttNPT/PnzgjyfeurJ1K9fv/Tyy5vTzTd/O/J64IH703XXXZcee+yxNG7c+HTbbb9No0ePTi1btko33nhjlOmdd95J06e/kmbNmpVuueU70YcXXXRx6tSpU7r77rtS586d06mnnppatWqdVqxYHuT2ve99L/30pz9Lo0aNTJ07d0mzZr0Z7TxjxoxoB334r//1v0kDBw6Mtn3ooQej/FdffXWUCzn95Cc/Sb169UoDBgxIq1evSnv27I0FQ38gSovfX/zFX6S//uv/mAYNGpx27twRbSSPCy6YnO6++5503HG9oo3/xb/4l0Gg/+f/+X+mP/uzP0s//vE/pr/8y3+XunXrlu6666707rvvRL9WxLg6rVy5Iv3wh7+ffvSjv0vDhw9P/fsfn1544fn0rW/dGK/HHdc77d27Jw0bNjy98srL6cQTR0f9LFgnn3xy+tnPfhpl0Yf/5t/8m9S6dUUD2lN7ffDB8jRmzMnRJ1u3botxqs18ZzGZN29+Gj36pHT77benK6+8Msar/nTeiBEj4nvkOn/+ezEmRowYns4559xow1//+tepb98+MeaQ+wsvvBBj7bTTTos6Gqv33ntv+va3vx0Li/RmzZoVY/LSSy9NJ554YmqOaNYRdUotYqKZkF27dk1du3YLEspkhbhOPnlMRNQm1M6du+IV8V511dXxuUmJYC+//PLUpk3biLbWrVsfkbn3vjvrrDPTSSedlMaPHx/RNXIxadq1axcR0/jx49J5550fpHbCCcPS2LHjIn8RpAWk9lI7b1dcffU1adSoExsidJ97feutN4PE27dvH+QAyvLBB8vS7t279hHugS+9fd2qVctYWExgbWDRyDDZlBXpWlTUwWK1aNGihnwRaZ8+fYPwZs+eHZNPWylPly5d0jXXXBNpHX/88VEHpCpq3LFjZxBqjvK2b98RBKhe8kV4Fh9tkttC+23aVEWorVu3St26dY+yOOekk8ak7373d9K7774bW0f6Tv7af+DAAenP//yfpQsvvDANGzYsSMKi8Yd/+EdRLm0Mb7zxRkSHSGrLlq3xGYJB+tW42Jk++mhtRMkWeZ95b2vJlUDHjp3Sn/7pn8b7U045JfLRnvPmzY2+0Fb6sEOHDmn06BPTrbf+JvXo0SPGo/rJ/zvf+W608ZtvvpW+//3vx1gSuSK4P/uzP48+UI8xY8bEeNR30rZAfvOb3wySle+kSZPT1q1bUvfuPdIf//EfxZWCsZXb25hzbK9ePYNQ5a0dlU07Vwv7kFgse/funUaOHJnOPffcSFP9LIICA/Xo3btPtIGIOG8dOm7Pnt3p2muvSwMGDIz23rhRv7XeL+quHc/6/eyzz04TJkyIBb25olkTdatWrSISMGAMqiFDBkcUmy87TSJ/IqfXX58Z0VCLFlW0adC2bt0mHX98/9jzdVmLABET4mnXDmG1jEEoDQTkz+cnnDA09ejRMyZWu3Zt43PHKk+nTh0jjVmz3ohzRelVXp9EPSa6SN1xiEEUK4JFKCNGjIwIZ/jwETFZQF4mlihKRCK/HCFX9UTMreK9KE6k26vXcXEMUkKieTLbJnr22WejLYYMGRqLk+NM2pyvqwPtIhITbSMrC5DIr2q7FG2HYESHiBw59unTJ0gol006U6dOTQsXLmqI8n3Xps0nF4L59wV5i/5cIVSkvz3IWdR38smnRCSp3iNHjkqnnHJqHCMiRTAWRFGbMvz85z9LH364KtIBpCNfBORPe1vckalxob0RszIgNPu7iLdjxw5R5vbt20W/uuJCvghaVDxw4KCI5seMOSn6Vr0uuODCiDDPPfe8+L8/Uf9vf3trXIkMG3ZC+s1vfh3bFvrRouhcC1T79h2iTAjQYiQiNr5Epbffflu0pStB7W7M/+xnP4tFGInm31bUwdXdxo2bIg1t1aVL5yBmCxUYvxYxQYW2MC5ckbhC9H9lWrbsg2hfY0e+2j2fi5BF6rZFjJ9OnTqnIUOGRNtWx7SL8fzUU09FuwoaWrUyf8zF5ktXLfY241sabHuIAgwA2wIuxxAVIjRJEF+Odgw6ZGAAi0JMCpGd451nIBnMJg6Ccpz/a17p5yjd//HQ0qXLIg3H+M55Ik4T2uWi8iAxk9lxyMwriDRMlpdffilNnnxBlCtHfSaM71z6ikClK8+PP16bVq9eE8TpO1cFJhZIG6RjAiEgyHueJs+AAcfHIvXQQw9FHoMHD44rEW2gXDnSl/bxxw+IKxPpWSC0D+JUHsSY2046jvPqfPUVifu/9heN+R7BaWd19F6Zclv4bOnSJVH3fEXUs2ePiD4tdtddd32QrvQtwhZG5XAJrx3VwzmdO3cKsluy5P3YOlBuZRDR6XskhlBsP9iisd2hfSyC/8f/8R/S+edPikXl3//7fx91VB7tqJ4WoFWrPoyFUxmcLxjQLvo5jzcR47/7d3+Z/uZv/j8N9TPWnOMYAYD33bt3i3ZSB2nJz/naRj8vX74i0tVmtuG0p7ZRD3UQlKhHXoyUxbFVH6yM9BwnTVdFFht9msexz3KfedUftjq6dOkaddYf2sXvF9hF2vkKofbYnj17Rn76UD8YZ/n3jTyefdZyH0H7Ths0RzRrov6qw4QxMb7IwYvULEImXj0DAWzatDEi/Nqrh2MNxDVnzpwgXVsootSjzQ+JiURtUzTn6LHg0yhEXVBQUFDnKMt2QUFBQZ2jEHVBQUFBnaMQdUFBQUGdoxB1QUFBQZ2jEPVB4NasA8lWj5VZk/SzRPtgPgk5v6PN03nSrj2/qc+OJD23rLm1q1ZiXlvWo23bw4U0Pq+blg5UvkP1Q+M2rRUkfVYcSf9/lr4tqD80a6J2v6i/A+Gll16Ke0+bAvWbv88Kt3URkJhQxBsEEQcCIxv3lx4NpE+a29iY55FHHmnw/TgSuIeYjJo3CbFILdw7PHPmzIOShDq71e+zwH3L7ln/PEC4QljSGMjv+eefOyhRa+dMziT2jz326KcWs8NF9iXJniyHq85zHk+OpszGCr56aLZeH5Xp0MMhYb3hhhtCSECd555kwg3eA27YN0mQNRmrzxctWhhKt0ossjfIGrnyJOCtALw23OMsDTft84vo3fu4EAG8887bISag1ONRQXADBAgMd/hgWDzmzp2TPv54XYhKEJr8CUgyiRCpTJw4MTwc3H97xhlnxL3N/CYoAKVBhEMo4d5cf8QPJjyZOVHBhAkTQ15uASBKkJ6yU66NHz8hxAxInOSa6IfQgn8DpRo1JjFGpSTbHOkhbOUlwnC8RSjXl0CDaRUlGil1NtyhcGT6c+aZZ8X92VU7vBceI/3790srV36YzjnnnGhP/cC3Q57k0e5fdt8yibh2I58mLlEHijrqSu0zfPiwkKJrA/c6+3OsslIlUswRX0ifNJs60j3YFImfSMZXxCKnnSktHUehaPzgbH1ESadNLWKIkqDqueeeC9GN+lK3ZkXq22/PDpUnYQ0xDDuDrD51HzX5tvMd+3d/93fhqaGMZPrMpYh2tBNBlDoOHky6fVKcb0waS16VWX+Tvyu7fvG5fLW1MUNgpczGirapFLgt0llnnRVtoO3dl66/jEN11ZannHJy2r17T6Snzx1jjFA0GqMWK7J67Sd9eVGI8roxrqRvfqgnPx3v5W8sVf05L+bJrFmzIi3lp3xsjmi2EbWBSP3Vt2+/GMRTpkwJYkBu1GuPP/5YDELHUXshBu8RuSiUgo3zHsMeg+qBBx5siHbJXpEJMyERlcFFCYgkkceDDz4YE5GJjeNEacx3mEE5xyQwwU32J5+cEpPNQBalIj5/5M7SQL49enQPiTEgR66AJOWiKQNcGXlbqCd/B5Hotm3b0xNPPB7HmCCiNZJr7aDeFrEczSlLNghSFw57lG4mp/Jb9O68846Y1PLig/HWW2/F+bm+yICEmWIuo1JIdogIXN5A1TZz5usNZljIBBERgDj2kUcejvSkqw7a9Jlnno429IpUKDa15R133B4SfBGuOkpbujkfiw2psnOREBOnXD9tkGGhpaZDzvpm+vTpUR+E8/TTzwSRsIMlDScBz9A2iFmd7733nlD36TMkrX2pRF966cUYNywIeGEou8XDooz4LELaXn9bgPiKGCPI8fXX34i6CRL0m3bUn9rHIqS/tR2Jt8XXFSIgYsRJuWkBUhaLsrGqLMjWd9nvxPHGgb8XX3wh2kfdvKq3tkC26sVrxcJP/ajPnnjCFcVjYY1Anu448vpsp2qMsEMwdoxBC4zjqv58Jsb822+/HYuDdm+uaNZEbYVnQGOQcCEj8UVKojnRooFmhefLYIKbTAyXRDWiqMoCtV0aPHjIPhOknTHAqjQ2xHGiYlJdElkD3sRAniaVySQKJ81GSAhP9GgSOEe0KBr1PhszyV906jMRHG+HoUNPiEkOfDpee21m+CVkqbJITT4Iff36DVFGdXes8qu7SJTHBRk7kmAglWEhsqiZZNqNOx7y8Jm2U+61a/kXK/+54f/hasPEyvX1HRc3+WTlHk8JxKZunxhPtYjyVFcdJ6RBgwZGvrYbqih8W9RNv1Sydq6E1X5s3taRj+hTW/lOeRGQurrKAcZXL7/8ShhBiSQRXK4fo6FadSEvDWki1lxO6VgQXU1Y2OWjvbVVhrZxlYAE5a/PkDYTKePBn7aStro417Gk/khVuzExkkaW9Vtw/J9fhisT7aytLJyO1wYCBe2jv40rbe8qa+TIEfvGvj34XQ12scYC7xD9Lvq1AGiXvG1SzYMT4gpizZqPYtz5TF8LbFzliPxF9LZ5XCFU/Tc02lRwo475ismCrR0Zk+UxXfX1oH39Wf1Wowz56mTgwAFRp+aKZkvUYGKJNiofjZ773MxaxLaByctQRsQjokJqBicCdtkIlYdC1zDKYUJkIALidenMM2LixAnp/vvvj4jH+chGNGhguxQWoSAVZCLaRaTVNkOPIGx5WAQqD5Jese0gquPjzNRGFHL//fcFuYDj1AExy8/kM8kZ8zDkQYLZYyMfL0o1uc4///wgUt9VniQtGsqAIJgZiURNcOk61yT0vfyXLFkabVfJn6tL+VxfVxSOz3COaBcRcSUUiYG6d+3apcFXxVaJvKUp7T59egdJ3nnnnRGVic5cCfl/ZWLVIfJRbpfKyrt585aIZJ0v0geLDDJAjtKWly0tUSRfEn2ft5qUVXr6OvuXZJ8W2yTV2GgRi5K0sk8I2PLKNp633XZb+uijNdGH2gT4kthGMwb0j75C/IhU21RXLn3Ts89Oi/yNBfaxFixlR248pNXbmM3lq/q7dTr99GphUjbjEjgWPv7443H1Jb1cXuO1bds2+7a0KoMlUA7Ha0NjBOkaq4hcW+tHfVF503SO7R/BijFvXEhfuyiftnAVpH7qBuPGjQvvc/MM8ZuX+tOxvtu9e3dasWJlLEDNFc1aQp7vXDAgsnlS/rXfgAHvfffJAKv+TLrzzjs39uOkYbJl06XaNHzm++yQVkU8lRtYto6s3OtaBgHm7zJR1r6C4/Kv+dkzWl7yz9HIr371y9gXNfkdlyOUnLby+X92P/P/7N7nWOXK9cn1z3VTxpxXLht49Z3jqu2eDumiiy5qqG8+P7erPJ2jHLkNcv616Wb4vLZN5aVN8zm5frX5+Dy3fU4j5+OYX/zi5+HhnA2JGtcvt3cuay5XLkdut1wP/0foXmvrmd9LW5/VmnQd6DWPjVzWnE521cv/z2M4lzl/V9vftWXL9XBObq/a+nit/c4f/3PbK6LkPFdyO+W+z/2f65evbvK8yf3RuG65v+Rpq0tfuFI4WPmbI5o1UR8tDDh7cCKtejPPyT9iiWy+jEEtf23jxzwRYr3iy26nrxLs31vs8o+rnxfydmNeAAo+QSHqgoKCgjpHfYWDBQUFBQWfQiHqgoKCgjpHIeqCgoKCOkch6oKCgoI6R7Mm6upxTZsO6w4BHgvuI3VHw8F8G9yT7Z5V6Tb21Wjq7hECg8/7xptDGQnlcmTRx+GUx/3R7p09VnAP7tH6mBwJjsbcyu1hFI+HOiffSULU4U996uWmqsOtNyEWFeaR4lDj3RgzZg6W/7EyOvs6olkTNaVgJXDY0XCvqluEGrum+Y4s1/ckvSagyZuNd/w5z/e8O1555eXwkjAws8Ncda9opRzLeUmHHN17n+dyNM47u8Tle6Yr1VbTx/peWWrfEyyQ6covp5Xv9f6kHA83+I0A1VhT7mv5nlf1I7bJdT+Q05zvHJ/rKE1/uYz5nlwKRRLlT8rcdHqObapcuV1q+6T2WHn7s9ASY+S6yys7ACpndrvL53klcmIpAI5pqu0zGGsxq/LkcJLzxv1xsL5Thlz+XJ6D9UG+v7i2P2vbr7ZNgLK2UioeuB35axjjuW32H0d7DlgO48Z4r23TnH7u+2wrUDsncvmkTWhFMp77JrfFnpo2bK5otqZMYJDwu3BT/Xe/+ztp9uzZ6f33F4fa68ILL2q4v9ZE9R3JN4m16BpxU2RRs7mfmvENGTETGuNJZEK1R+rL0+OWW24JFSKvhltu+XYQ9Pbt0loVCjGeCAbkBRdMDuVYzluEzo/i8ssvD+EBGS9/BfLam2++KfXr17+hPgY3lSIPDCY9yrN8+QfppJPGxNPDKQDdD0sVSd1I0UdGbUIgSRPIREaaSEb7fOc734l7jTMsbFRkJuWECRPCX8PiRBl3/fU37PfEaISoPUnTSeP5eUyaNDnqrE3I1OXJc4N3BYUfxaXykD9fe+11+6VHVs3RbteunemWW77ToAQFJlY8TGDy5Mmh3EMM+uuqq65KU6Y8uU+y3SY9+OADoZZzhaRv2QhoTzJ8qjqeGsyWbr752+nuu++OdqUY1SYUgtrl6quvifFQew+292effU4cS+FJgeh8dRwxYnjcVz5jxqvRDzfeeGP4d2QgIr4dFHsUoMZbJf7Zk773vd9rEO1oB4ukcjhffRzPoInVgbKLiJk1kXBzKZTORRddnB599JEYa9odIZLiS7v2vmVj1/F8NqgPjUXl59/iSevmRu0VqTRJx/WZeWC8qz9lJ4HKpZdelqZNmxpqSupX/Wx8CR6uvPKq8FehWtUf+opvCgUjzxNkLv916z4OK4YLLrggVIvNEc06okYCF198STyp2mpOvkum+/bb7+x3HLUUqTC3L0RLAEDQ8c1vfiuIwzkk6O+9N7/h8o+RkwnAh+PUU0/Z59dwXHz+3nsLgkiQIKk46Swi45D2yiv7G88gCQuDyYPgEAufCg5rixfvb8GaHew6d+4UE9bAB4RiMpH/cqozqbyS5SIn5SCJ9rk0TGILC/lzYytXRkSTJ18QCxnCmTbt2ZB5cx/MkVsGYlN/RIjclHvs2LEhVbbIidzI6G+++ebwi0D4rjp+93d/N9L7xP+jIg/GWN/+9rdjsubIP4N/hn5EoDNmvBYqN2RIMi8fviv6kafF6aePDcKRn7ZhzMUp8YYbvhGfGwcITd+SqN90001hKmShsGCed975YSh0KEifoZAFe86cudF3FiOyaGZatbC1whXPgscV0LHf+MY3YjE33mrBdfGKK65Ml19+RfhkaHdXCghN1GxhtRAjN0R37rnnxXacBfrCCy8MQtUnFl2BwKdBTn5c9Ou7774TC6PPGj/t3iKij6QnbxJ9Y0obalNjyfjlD4LsmVY999yzca6rOG1s4dGnpP6k6VwUpasNuBRagHfsqPxLtE1zRTMnal4WbVObNq1jsPMzYHjk88ZoLF4TLVRRzt6IhhFxltdmmGzTpk0LAjGxTSaD33H29FimmijONVGRYjbfyZAH3xBWqsgo+2/43ICuRfv2lVubKB/pI2cyciY4ysXSlKUnEly4cFEsJFU5FkU51FHdXWHYszbBav05wCKQLUcdK33yYtG0ctXC9omtAMSo3HwoTGaTlQGW/JRh0aLFQSS8JUTqVdqtGqLIqv0r7xDfWbiQQ2NwNERQonFlUU9Rn7wuueTSiEIRizLIB7kgE+WXtrJoK+SCFLSnKwz9ZHyouzTlo58Q+cH21S1G2ue0005Nl19+2b7x1i7Gm6udWmgbjn/jxo0PMmUEZZxkmfj+x3aKPlNXZc792apV68iPrcH1118f39WOFV4foloRrvcHsgy1MIiMK6fDFjGOLOS1zoBgLF9wwYWxLcSytbryWBcL1KhRI6M8FtQPPljWcI50XMlddtnl+yTpbffZK7SMV+0tT/UQjOi30047bd/VyIzUXNGslYkIBxEhK4OWXaOBZmK7bM2XtZoIUfLGRRSiDa5eIgf/NxlEj8hDBIeQneNzl7AmofRcaotKePLaEhCdi2xGjBjZYOFo66Q2cpCOKLm6RO/VYNKUnwSzv23o3oiklWnkyJERxYiabFEor0kgopSXdEympUt5Ea8JkuDu5hjpK6tLa74LtTJ5xCSyFLWpf/XAg9nhLCcqzm1m0v793/99eFy/8MLz6dprr41FxASsLv93hu2nRUoE73NlE1kpE3Lj+FbbB7ZnbPvY7pFXbblcldh+4mR4zjlnx/aPCFxfIS7fIwn1YcOpjUXdSEDEbbuEO5t2lkeXLl0jD17ciEvb2zpxjvZh+GQLQps1jjSNI+mrk/MRlf5HStlt0F/tZbz/2/NXR2UU4RsXiI6zXO32hIVGHWyvKJO66c8TTxwV2w7qw23OOM4+MFVUvjctWLAwFiZWua4ELaK1C2x+SIFol0ueyFgaeRypU4btPeRpHGgT59kKccXw1luzY7EwNpzjitR8q8o7PRZiWzC2V/SPRU89LZT6xMJtu8V569dvCJvWM844M8rcHNGsibrg84NhZU9edGXLx7ZLNif6PJDvWrFAHQ4sOJ5OY8GzJWCRKJ4fnw0Wg8o/fVc66aTREYmXNj02KERd8LmhNgb4vCdszutw82kcnxRCOXZ9UNr02KMQdUFBQUGdo1n/mFhQUFDwVUAh6oKCgoI6RyHqgoKCgjpHs1YmfpE40h+7vk5l/Dx+VDxQWb/IHzC/LHyd6vhVmBf1gBJRf0Fwf3Ptjf+NQcBAhPF5IvtHHAiEJPnhr8cS7pmmmDuWIPZorJrMyE82P1pkb4p6hfuaCancx0zs8lW4w1YZqQ1r1as+IwRTj4KDo1lH1Fmh5iZ63gOUeG7eJ6Rwg39++jYRBu8EogeDiiiCkIKqjfcGRZXjCR/c3J+PIQIhNnCjP+GDG/opAwli3Phf5bMkhCCIIT+LkehAurmMSEe53PNLIECcoszS9ZpFCI7N55oQldqwRZQDkDAxBCGC/PKxxBkk9NIjjCHioOgjeFF+dePFIC955IfKum82PxiWQEEbEllIS5oEHdLTThSKZ5wxMS1b9kH4QJCRK5v08gNQ/V+aPpMuMQzxiO+VJT+4VTlNek/wVn7lq54aX6kofUesQq6vHQYNGhxp6hftrvzSkbb6EY4Q+lRPOe8TbUTifdVVV8f91tJQf8fLx1PS1cOiR3CkzPpb2j7TdvqLkESfOk+e6qPdjas87uSpfRyvLI7XZvo8PxFe/zpWOsaMz40nwinybuXznfbWVt47x3jMfSdvSlFybJ/LnwAoR7LK5VjH+TyPYe2uL/Vxln37LI959XduFo3JyxjWntkXxHHKk8259B/xDW8Sqs++ffvsJ6TJZVFn82/DhvWRvn78vJ/bWK9o1kRNSmtAkKlylzMQqMNMGAPKIESkpLJ8GC655JIwDTJBTGbeBCaMAYoUDWJeEL/+9a/D04ICiySdbJfCzETmB4LUvJLdmgDyQZomHBMd4gumOKNGjYr0yYkZ4lCTUcwtXPh4qLT4SFDhGeTO5diGZHlBVE8Zbxdl+uEPf9gw+Zg0IQYmTfKZOvWZSPv555+LNkGgolW+IhSF0qOgpMQziQlL/CELZDJp0qSom3SGDRseRKBNqQ15ZNxzzz0hFUYMK1d+GMTD0OrP/uzPggAo7JAtIyHlZUCFZEXLPCAoOCkzmTDJkzJQX2jDc889N913331B0GT8f/AHf9CgstMePFWUTXrUe161tUVGvtpi7dqP42np2m78+Alh7ISItBvC/PGPfxxyfAZTVKTy4csyZ867acmSpeHHoT3VS3QrzQEDBkafKIv+46ZoPFFNSle9tIHxxCDK59SQN954U5D+//V//beQWL/00oupa9duQWZ8ZSweFH/XXHNN1BEZcrtTFwskInXlxodGH1LJIltjRd/x/iDyIT6iPFV2MAazUEUgIO9bb7012ku5PKndePrtb38bY9jVkcVfWdu2bZPuvvueNHLkiDR37rzwiHnooQeDhN9+e/a+J9i3ina3gBqH+tZY5lfSGI7PToXvv78klK1To0/6p6FDl8d4a45o1lsfrhi5ivH7MPGt5Mx3kLPJa5Awmbn44otD5rp8+YqIEnxnIHLz4hdhMjL9ESmZkEOGDE6XXHJxREuIhoGRycJvQT4bN25Kq1eviUkhLbLxbL7Ep8JnzIkQoe+YGSHOd9+dEyZKInATRTCENMGERJCk0yIR3gk8I5SXV4jIiQERWbLJIBqVD0IjheYapz5ITDlM6soHY2QQPL9oMPkRBsc7ij5QBr4M0hMRkv6KgpAScr3ssstSt27dw5FOtKnN8haMevvLFq6ibceL2LSfNDZs2NhgHiRvcm8udY5HmAhDfWptOPUl0nSeRYIsm8FQNjiy0DLGEh1rD7L0Sy+9NNpDO+nv7t27xULFjAshbtlS5SPNSv7fKyJI9eVER4p/6qmnxSKs7FV5F4fRkPLqfxFwtl21OKkf8yxXbNWY3Bt9ZhEiq1Ze/Y2EOQ7qy5x2tiu12Bij+kN7KKdzSOLV3ZgDfUEWbgFGwjkNYERWjWHtuDsNG3ZC9GfejjPuLBjVMd0iiDBOOCMKVJibSd9YokbVn1zxXHlIx0LPGdCYtqiJkI2RpvyyK9n+tiB155x22qlhLKV9myuaNVG7jEIIthJEvoh2yJChMVFZivIdcFn22GOPRRQnMhRtI0Hk7jKUf4MJwjTGeUhSZOgYx4qWRCuiQoNXlOByzvkiSfmYOMph0iNH+ZkwLlF9ng2D+EVIWzkQHS+MbGgvX+l5VQfRnPKJeJQFeI4gusqJb3XkYzKQXYvC8z6yyVaVsV2Qq2gmLwgmJUm4ieZcUJ7p01+N9BAgU5327TsEkSFmVrIWHlGeNvdd/u3I5GZp+dprM2ORUD/QPhWJmfDtG3w91O3pp5+JvU1t4qpGOXPfgFcLl7ZU509+qGoR2yHAQ0K7ip7VQ36Oc652ZxuKAKWtXK4+mDTJH+EiZou6RcxWQ9VXbaKersDUO7eNcWRPuVqIuoX5V97C4qVhEXNclHCf6Zb66m/lsvDbTkByrkI4yeXxpy0tbmxzjbHa9shjJPddleZJseCLjF21ZSI2hvXNoEEDY9HQDtX4qdoU6Va+Jp3j1cLBXIlBlYVX/iL80aNPjO2hai4NibFTzaUXIn1kq+2kp119Zl7wCDGO1Z8tb1WWQVGWNm3aRFm0b3NFs1YmiowMAn+iFAMNWRqcopDKha19TDoE6PLROSZlfjVgkYHjTXyD1GeIDpnl7xzrTz4GZibCKp8e4ayGpERQJr/9RhO71pTIwFYWaTpf5CI//8/1QU7Zgc+kNrkrx7bKDMj58hPR5Xx8Jy2vzhHhKqc81EmaLl2RdL40RTKiJOXLhkkgbWkpE1JC9Kw6/V85HKcdRGUuieUlWjdxHSNC1NaI1v+Z8VRk1K1hL1ub+c6fCex8jnK53souD3lpM/VADraa1MM5+SEPebHILobOtXVgmwbZ6Ku8t5zzYWql3gjKFQfiEoUiMeRYkVj/yDu3uSsr5kKuciySyqSejrXFYtypT35KzCd5Vv2tLN7rA99l0naMfNRFPWrHiO9z32l7ZWYrK8q94oorwv/a9ofv1FP9BB62baQvjdwuYGxqU30sH+WW9s9//vPY8ss2wfrO2FIefZbHuLopkzTMMccqvzLaFlEmZVQW51iMLGK7d++OdCoHw0+80ZsTmjVRFxw5DJf8I1JjW9PmAkRqWwJpuho5nB+4LACIDnl9mUDiFjEkbFFRh89ya5zx4KrO1eTRmm65CnHV11x/KDwcFKIuKCgoqHM06z3qgoKCgq8CClEXFBQU1DkKURcUFBTUOQpRFxQUFNQ5ClHvg9uo/JpfexNMvlXqUDfG+N4tSW4jqv3MbUaHOjdLub0e7FhpH075vHeHQePP3GZVW76CgoKvDpo1UdeqoRAeOXLtd8j7zjvvaCDRpv7A/Z1Z0JA/R4o+I1TIxjmNz/NKHkueTM6eSbepP8IE0tzGCwFJe14Qcr7KnB9+m/N6+OGH4t7WA6X/Wf/cD0yu3VQ9CwoKPhuardcHEiE8QJIdO3ZIEyee0eDs5Tv+CTwJyF2REC8Mwg1S3ZkzX0vr1q2Pm+95Dzz11JMhlnGsNPiFkB27r5QqjfLuT/7kT4Ow3XNKceYJzEQF0j3ttNND1GFB4MFBJkxB5l5lii73vZJ4yx8sCNSMntZNpCBa5hFCbkuuTkae60FIwHeD0m7SpMnhpUEcwYuEQMFCQdHoflqyYHWWr6dLE6sQYqxatTrK6/2zz06L+6fJuKdOnRZlpSSzkFCX/fEf/0ko4QgWPE3afcMifE++LigoODo064iatLVNm9Zp3rz5+1mQIuYZM15Nl19+WSi1EDPPD+5jPn/jjVnhxYD8RM0UdrwOKPYYJfGPoLqqHPOGhs8EhR959bXXXhukKi3kOHz4iCDB9957LyJqxEq99tJLLzV8z0An+zuA4ywGvCnIapn+WCh4YpARZyB0ZlOOY+5jkUDsTIamTJkS5eVnonwEHAi/kk5/FOU477zzQ6qtrhYcUnDiiEWLFofM3DEXXHBhfEedh7B9TxBz9dVXxQJmsSK1LigoOHo0a6IWgZLmkr8y5cmoLttTkCC5cZZ8jxs3Po0bNy7k3og3f8fdLv+fAY3IWOQJiJ7HRJaDI65KwVWpwaixsp8CkErz+ZAH7w3Oe9Kt3UbI5kNVnpXklsyXgRPS3b+O5LeVLan6Vp4J/l+V3ec+84eobdXIin8E2a+ou/L0sNWyOyS9XN+YUpGBZ/m7PLINqbRataryzJLngoKCo0ezJmp2jqI/0STCzJfn5LVjxpyUHnnk4XCL43yHfPg5s/EkvRWNM+gR/XKaEz0ifC5rtgFGjToxjHyYOtniQN62GNh+nnfeuQ3yaxG3/ORji2PUqJFBcM7nfsYjWz7IzjEgWmcpytyHhPmcc84NB7bZs2cHics3k6+o2HZKnz69w7BHXg88cH9cAbB3tfXhikFkbRuGlwWPE+5x2sSrhYSnhcicU9zcuXOijPLOZeXWhqh5Xijf/fffF9swonNXCwUFBUePZi0hP1DVs9HQgc65++670w033PCZvAkOlsehzsvl+Lzy+DxQHrVUUHD0aNZEfTTId1aIJAv5FBQUfBEoRF1QUFBQ52jWe9QFBQUFXwUUoi4oKCiocxSiLigoKKhzFKIuKCgoqHMUoi4oKCiocxSiLigoKKhzFKIuKCgoqHMUoi4oKCiocxSiLigoKKhzFKIuKCgoqHMUoi4oKCiocxSiLigoKKhzFKIuKCgoqHMUoi4oKCiocxSiLigoKKhzFKIuKCgoqHO0/rIL8HVC7cNyDufpL0d6fFPn1p7X1GcHyq8234OV40i/O9q0vuo4VN0+a92/ym13qHF5tMc2JzT7iPpgz0acP39+PJjWew9o3b59e5PHOu7jjz9OTz/9dHr66afSrFmz4gnfh4Nnnnn6gOkeDHPmvJteeeWV/cr/9ttvpxkzZhz0OYl3331XWrx4cbrvvvvSli1b4jMP+PUw2qawadOm9Otf/zoeP9YYnlr+3HPP7veZej/55JNNlmHr1q1p2rTq6eyfF/RXU2X9vKHe+t9DjJuCPn7mmWf2+0wb6YP8VPlD4dlnn23os88LCxYsSGvXro18Dvcpfequ3Q+E996bf9gPOH7nnXdifBbsj2ZN1Lv37E2vr9matu3a0+SgnDNnTnrnnbfTjh3b0xNPPB7HLFy4MAazCbls2bIYgCbahg0b0vPPP5+GD6+eZO6zRYsWxvEIygBEiI3zkZZB7knoOa1ayMfTz9944404zvGeNr51a7WArFmzJk2fPj0+8yRy57///vvxpPB33303JpG0p09/JW3cuDG9+eabad26dUFmzkfur7/+enzu6enSsvBIR9lnzpwZ59aSn/PURR7z51dlXrDgvaiv/EzM2np6r620p6e0O2bu3LlRF+kiBuVHDh9++GGk59X/5fPuu++k1atXR320hwXi7bdnp48++qjhGO3r/e23355eeumlIMbcpk0RtzJJU721i3OlqR/1l3ZZtWpVnCuN2bPfirbOn3nVXtrIe20mCvTkd2X3/xUrVkQ+y5cvj7rPmze3oT98L88f//jH0RaebO+18RjxXvmURxs5Tnrawvv9xvPu3ZEHolO23JbKJ9/cB40Xk1wmeae0N9rgRz/6uxhHxspbb70ZbS0/7awdchm9Gj+33fbbKKfvtKk0pFvNoXfT6tWr0sqVK2Ms6z8BgPp4rYWyGg8ffPDBvnG+Neqib7xvrmjWWx+79u5N/9OLy1L3dq3SX43vl8b37pha7rvkMunGjx8fBN2pU+c0cODAGHQGjwE4evTo9NBDD6brrrs+Js6gQYPSrl07Y7Iguz17dqe33pqdRowYEZPXgH/ssUfTH/3RH6euXbvuVw4D1wBHitdff30aPHhwwyR49NFH0+uvz4wJN3r0SRGxjxp1Yho8eFBav35DeuKJJ1KfPr1jop177nmRlqekd+/ePQjz3/7bv4x0Fy5cEOUBZRQJ9+7dO91++22pd+8+MUEREiIV9f7FX/yL9N/+29+mkSNHfSqKM5lE4AMHDkpbt25Jr702Iwhr8+Yt6ayzzvxUO6vbvffek4YOHRrHiCy3bdsaVyEm8GuvzUxjx54eE3nKlCfSD3/4+xHxn3322enxxx9Pffv2TRs2rE9dunRNmzdvCtKS9zPPTE3nnXdeevLJKWnQoMFp+fIP0saNG6INpk6dGmVDHtp+woQJn7qcRgbI2hVQt27d4oHFPXv2ivJpGxHyueeeG6Qq7cmTLwjCvummm9MDDzwQ/aS9rrjiyrR+/bq0ePGi1KtXryjPKaeckt5++530O7/zO+nOO+9MI0eOSBs3borFXL216YABA4KEvHdltW3b9ijTt771rdSvX7+GMXDHHbdH233wwbJoo2nTpqb+/funJUuWpMsvv7yhXq+99lqMA/UfPnx49Mn3vve9GKfnnHNu9Plxx/WO8XrRRRc1nOfKrBrDA9Pq1WtSnz59olwCA4vNxx+vS88993w67bTTonz6RT0zKjLdEPXSLvrSVdhJJ50UY0XaPXv2jHNXrfow5pF6GaP6tnPnzvv1i7GuvR2/ZMn7MX4GDx6Sjj9+QOrQoUNqjmjWETXsiKh6S5r10dbUOKg2iLZs2ZpefvmlNHbsuIboGNG2b98+Bs+ZZ56ZjPeuXbukfv36B6Ej6SVLlsbANtFFDQYc0mhqm8PnFdFtiklWC5HThRdelC644MK0aNGi1K5du/SDH/wgdenSJQjXOSbvxRdfktq2bRt5K89NN90Yk/mjj9ZEtGvSrV37UUO6zl26dEk6+eRT0nXXXZtatmwV6ZhUyiD6MpG+853vpI4dO+5XJuQ6ZMjQNGnS+fHde+8tiAi/R4/uqW3bdp+qn2gMsZ5//qTUqVOnaAvEMW7c+CAW5bbIVItdFelbDL2efPLJafz4cbE4ac81az5KCxcuCnIbOHBAHHPqqaemM844I9q5f//j06hRo/YRyjlB0AjOcbWRKjJArB9/vDYIZuzYsWnFiuURRWqvTKSuplauXBFtoT1rywfnn39+kJA22LRpc0SSFvZJkybHmPjww5VBupMmTYo+e//9xUF0Z5wxMSJHaY4cOTIWSYtX3759YsHIkI/+kN6wYcOij5BoixYtU48ePfZr52XLlkY7qHPuY2XduXNXtIeytW7dKnXv3m2/8/THmWeekc466+wgb+U0luVnHCDbdes+jnIaRwKADMerg4VEm2oLfbt79664wlLXiRMnRjn8f9269dHeyqj981VHLdTZ1daGDRtjkTjjjDOD3NesWZ2aK5o1UYuef3hiz/T09aPSH4zulVq13D/iMmFEzh98sDwdf/zxacyYMTEwRQAmSceO1eqOnFq1ah2rve/bt+8QkfSLL74Q0YwBaSz6XHRdC4Rv4G7fvi1IuHXr/S9yDPIHH3wgovHzzjs3JpHJJs+2bds0RLsGsbTloXytW7cJwtixY2dcAku7Y8dOQZRt2rROnTt3SSeeeGJc1t5zz71BuEjapLYQDRhw/L5L83+MvGqjUWRoIol2U2oR0aOIXJoi08ZRjwUPQT322GNxHNJ95JFH0iuvvBzk4FL8vvvujYWoQ4f2EZUhI23Rrl3bqIvXNm3aRDn1g8l93HHHRftV7V+1CUJ1RXPCCSdEfi+88GIsRqJc7ZyBVJCXRUJ5RW3du/cI8lQf34sa9+zZG4trmzZtG7ZdHn744SBB5yqbhRYJ5jLWjgNXK8jokUcejTRPPHF0evzxxyJCPfXUU1KnTh3Tq69OT6eccmqk2a1b9+ijDG3Qo0fPuIJB0qJ4Vxbqrf61/WLbberUZyLi1j4WzgcffDCIVntIV5l69dr/PFdNrsy0V6tWLWN8aM+ZM1+L7ZCqTh1jwTXuV63anzCNNySsfoKBBx64P+o9ZszJcYXkikt+y5ev2Nfe7SOtrl27RSBRC3kpuzHrWOP2448/Tp07d0pr137Sf80NLfYe7i8GX0Mczi/pJqcoIl+eiSyQmcjBBEYcvjfAvJoMCM5kdcndsmWLIEwE4DPf54gp/5hUDca1QTTSk1aGwY9EkLA8EQIitV9oAP+H//C/R8Th8v2v/uqvYgIrc205qgisReQjPRPFFgTSlwYyqIiubUM51VeEijyUx7G1d3Yoh7Sk6Xjl3727iqi0g3o0Pl5ayufPRJUuYne87xGjaArxSVPazlE2r9pAWl6dLw/H+U4d1Nurdkco6paJM29TqWMuE9KVdyZWZUB+gECQmm0Bx2zZsjkNGzY8Luflo2zK5U+eyuNVmXzvNbe/dlSv3Fba2HkWlSrtLfHe5+qm7HlBV059rQ+l6U+5/WnrXJ88VhClq5Tt23fE1VztecohL+3ceIwpv/JVi0/rGNu5nvojj0ttqnyOq+1f5xsjuf/koY45XX2urtLUBs5RHsfVBifyla5tK+PJsbt27Yor2Zxmc0SzJuqvOvKPetUe3uAYyOW2pk8DESGIWnI5XCAI21LIxuV94yueegSiRG6Nt0YKvrooRF1QUFBQ52jWe9QFBQUFXwUUoi4oKCiocxSiLigoKKhzFKIuKCgoqHMUoj7EXRXuXfUr+uGisbDii4Db09ySdrC8fe6Yw/WV+KKgPBSdjWXTR9OGhzrns/SNu0aOZBwcTf75NrnDLWMlZtm/7T5L2aR1tOPD+W4vrJWXf5Y7dMrNaPujEPUBkH007rjjjri/Nk+yQ/1ldd/Bjmkqr8/y555aIgP5yr+pY0wAhkx5EhyqXEfz/dHU0eSeOfP1/Y7hB5GViYebvuN5TKjngc5xTzGhTm2bHG65tSvRzMGOzccfqAyk7+6JPtjY4aUBOY2DtSO88MLzce/x4Y6VA6UHDL200dH0o3H1m9/8JoQqhzM+DtxH60IgU9sGew9Rh+aA+r8p9EuEgUsCjAip6CjcCEuGDBmSXnjhhVCVjR8/IVzN3G97wQUXhCqMX8IPf/jD9Oqrr4b3BJnxokWLQ3p9ySWXhrghw8BjxkOdRgVJZMGAiail8rF4MtI+66yz4j5gx02YMDGkvdJ98cUXQ4RhshJ1EBHccst3Im/lvu666yI90mjnIEZlN7Guuebq8LOQ5yWXXLKfCIIwQp3JvnlEkLHXig2o+p577rko2xVXXBETnNcEBdk111y7nxBDHR3P54JwgjTZVYBzeJbs3bsnFJLMe6gOf/rTn6arrroynXTSmKifdlYOvitEE+T85OS190QzIvrJT36cvv/9H0QeSJsikm+Fw/QJIuTV8Sd/8qdhXkQpRxZN5DJjxqshHKIorYV2sAiSMxN0IHrHUjFedtll+7WJejKw4g2iHu3atY9zqE5F47feemv4hZBP8yKBq6++OkQ1hCrUg+TYlTHWvKg/v5naemZ3wjyWiEL05ZQpU9KmTRsjfc6KpPYUhpdddnn0Ma8U/UIkUwvtxiKBnJ9q1Jg5WDtrj/vvvz/k8ZSHytqlS+ewU9CmPESUhWBo4sQzQjFLXm/8MjiTLrsDZKz/Casuv/yKOEdAxJKBGtRira9YMgwdekK014knjkpnnnlWao4oEfVBwDPitNNOj4lP3owMeTKQP1ceF0yM5sdERKokt8jWoBSNc5cjvUWMJp5J2FhebXIz5Ln66mvSiBEjwxDohhtuCII38BErEkXQTHhImRELcjdppWuiUrLJm7eC70160QmPhDffnBVmT5WEuF0Qpej7/feXBIETcjRWfFGSzZ07L86nRmssfafQNIEnTBgfCxqzKIuCiJFApDGqNusQRCcvpks8JnhWKMeCBQtDpagsJiSfDrL53M7K+eGHq8KESFs0Bl8O8uxTTjk5JPe9e1fki5huv/2OWHD5eYwbNy7yl+eNN94Yi1HlN9Iu2q1xxIZcSLjPOeecIDKLoYXWwlIrSc/o27dfmAe9+OJL0cb6j4Sb4RLpPLWgPtZP+kH++hLBkvs7vl+/vlGfl1568dMTtmXLNGTI4FhU9SvfDVG4VwsD58B58+ZHeW2jIF5jAUEaq43BH4TPiwUNadseO1g761+S+IsuujgMw5C0MWK8GH/GmPGgjSzw+hvZW1i0B/hem1v89YP+1ZbGfdU3i6Mv+Mm89NLLQfbqWy0mzROFqA8AJFTJg1vFICG5Rr6sRMnCuatde+21McBFVwaoSzXeD3nPlfcF4jCor732urAEzZe2Te3LZZtSBOyVfwgDHVGH9ERiBjBPBlGoScXkZl+JI2/ydpNARGLyMGmqTXPWrDfDSY8vBbWdyfHoo49EWvsNjJYtIyq86647I7JqooVC+UYeLzrOeYjwmlLvSU+bXXPNNalnzx5RNtF4JkZXI4x8RN3IW1peczszK3KVIb8mB3IsJHsb8mcqhIi9tzgy19KfTJWUVZmrBa5VGBkh9MqLZH8onnJVW0aV/0ufPn3TN77xzVhYaqHMFgmLof6RtyiRN0a2QK22dKq2Mk4svCT92tIxPud1gnSbusw3BkWXFjB1zTDWmFNNnjw5PGCUTRrI14KlDZrafz799LHh68K/JONg7Qz6T/58QXh6XH/9DTEPsqTeFZArlSuvvCrGpDHrc94ergZFzcomH+Mw/05hEchGUhZE7ZWD+T59eh+VsvTrgkLUB4HLVkY3vXr1jAiNk53tj6uuujqiQcTbrVvXiIyQqcE5fPiwiKAQDDe4119/o8E/WURp26QWBt6ll14W+5/Lln2QLr30kiBObnMielEYQmVxSi7u8t3lsMjMNgtjJXkjVfmLWpTb4mLys/9E8CJeZkPsL5EBkylk+cYbs8KUp7HVpHJJE5GRpzeeIIjN5wjBdoFLbGZDJpSouDGuuuqquCIQ+a1YsTLdfPPNcWlvUjqfY5124pYn8mXNeuWVVza0s4lti8dkdcndGOrrikQfucRnR7t0aWULympUvdXRwoXQ9dMjjzycLr744thX9Tdu3NhPpavNLNLaXsQobQuhK6zGQEbaTFqnnnpaRJLqDK6mRPMsa136u/oS8YqsjSmkrk8q86+T4wpAGo2hvUXttgcqx7q9MQaclz3Lna+dlFcfIV2GXP37V9apteAsaFvKmDIO2BAcrJ2NRf0GrgIrf/b3IqBhLKUc6uN3B9s9xqQgxoIg+rZ1aNHTruosD/VA9IyhtKExr+7Gsiva3r37xHxozigS8oMgRzSNH1f1mRv9II+o+rLQVKTiElRE5rL8SMp8qEdRHQt8Xnkcad98EX3Z1OPWAEna6mBFe7hmRceqfgeaE0f6+ZGgRTONpqEQdcEBkS+VG+9PF9QHcv8gsOZMYs0BhagLCgoK6hwlVCooKCiocxSiLigoKKhzFKIuKCgoqHMUoi4oKCiocxSi/gJwIN+GI0Vz9jooKGjOKF4fBwFSJMJwexpll/fgBv7ah39WKrpKyeihnJ4SDXw3CAdefvnl8Cwg5ZVGVstJgxrLeyq9Su3VKs7z2vgY9zST7xK7UHARNxC3lNvnCgq+3ihEfRAQFDz11FOh6KKMopZDzuTOVHhMfygNmRMxo+F5QEF33HG94pXPgXOpE3lVIGjqMf4KN9/87fSLX/wilHwMg5xDmiy9N954PW3dui2Ueb/97a0hh6YipL7jwOYYfhE+o3gjGy730RYUfH1Rtj4OAg5sF1wwOWTDvDO4xCFkhjwi3m9+81shseZdgEyZy/ichJzzFx8N8nBSXqZNJMy+X7v24/DW8H9p8EbgHbFixfIw6an8KHanjz9eGxHzTTfdFG5k5Ljk4CL4xYsXNeRVUFDw9UaJqA8CZjJvvvlW2rhxU5gjzZpVeSnYxuByhihFspzXEKrjTzppdBjkcMx7/fXXwxNDJCw6t5XCfMbWSTR+69axbcGYpzKgaRleCfa0eTT069ev4Rjf2Ubhf4D4e/U6LnwlGNu8++474ZVw3HG9v6hxU1BQ8AWiKBMPAtscImOGNrYbON9lsxuEibDzQwXYOjLuWb78g3TCCcOCjG1TIF5GOiwmGQN5ZU7DLAnZ27qw72yLhIEQAxr5WACY7XBLkwbjHMTNbEg6bCEd7z3XPfviFouCgoKvHwpRFxQUFNQ5yh51QUFBQZ2jEHVBQUFBnaMQdUFBQUGdoxB1QUFBQZ2jEHVBQUFBnaMQ9WH6azT22Gj8eX5galPnVw80/fI9OpQh+474I09v6oGntcgPra0tf34Y7+HWqXro7e4DlulwPEwO97jPgvyQ3mOJw23nesSX4S1T28/F2+YTNGuiPtRgMHFffPGFkHGTddceR/jy4osvNvzfA09fe+21JvO49957wq/j84R8li5d0uBH0hRWr16dfvazn0W9+In80z/900GPByKfO+64Y7/P3Pf93//7f28g/UPl+8AD96eXX36pye/4oJDpHwwWBcfcdttv09y5cz838lDGph5a+1kwf/689Ktf/TKtX5+fFn/koGLdsmVL+iKgbT2cV9++9tqMoy63B9saY0eDJ598Mt1zzz3xIOCCCs1ambh7b0r3LVqXJvXvnPp0aP0pvwzCE0/AJgU36EjEiVwQFYELE6Xt27ell156OQQwvD8QCfOkU089JfXvf3yk89FHa+N8DyJt27ZNOvPMs0JxSDI+d+68eOL0ueeeFyIWwhZP5V679qN4gvX550+KiUq27r1JZPKfddbZMZnkRUZOxv43f/PX6YwzzgjJu0WEIvKSSy4J5eKCBQvTxIkT48nkiM8TzLPplCd3y0Pke9lll8V5iGHKlCfS4sXvp71798QTs6dNmxZPVyfAIeyBjRs3pP/wH/5D5KtMyt+3b5+0bt36aKdJkyalNWs+SosWVYZSJPU8ULTT6aefHsIgykt1kL52vfDCC0NYBMqo3eRzxRVXpm3btsaxPFO0N1m9fiK3JyIiOtIWyus4ixLZPfGRyJZoiIozS/iJjqhGPRWbeMkT0vmynHPOOfuemL4iTZgwMRSmoO093dzTurWDNjMOEBODLrYDVKXKpX+mTp0W36vH008/ndq1axvpKfPmzZvS4MFDIg8Lv7qrp3bUJ9WT0cele++9N2wGqqerv5V27tyVzjrrrBA86X9tT+wkjenTp8fT7j21fsaMGallyxYx3ixAFmrpMADr3r1HPG1cO2orbaSO2uNv//Zv0403fiu1b98h6sfjhoXBiSeODk8aeVDlSlc/aT/jQv7aW79YnP2fNYK6+FyZBTTSoqwl5vIEcopei4J28rRy488YJTTTZ9OnT4+2dD5zsuaIZh1R7967N/3XN1elSx6cn+5euC7+X4sFC95LY8eeHuSGoPlwmMwzZrwak8TgQtIGpIGL0LyaDI888uj+ee3enXr16plefXVGnAvvvbcgolUk/vOf/ywiiVdffTUI5uc//3kQtwj4V7/6VZg0GeD/8A9/nzZv3pJ+/OMfxwR65JFH0sKFC9IzzzwdUvSuXbtFeZDGY489GuQncu7Xr29MyBdeeD5MpF57bWbq2bNH+slPfpLuvvuumPD3339/kBRSefbZZ+MYqkeTRHkQx+233xZPJ89o1ap1Q77a48knp4Qh1Y4d29Pq1avSnXeKxvemLVs2h2mVMlkctNU//dNPggyR6I9+9KOYoE8//VS0by0skOPHV0TSt2+/dM89d6dhw4ZFBDxnzrtBgB06dEwPPfRgEMdzzz2bnn/+uSiDPuTT4jvESta/Zs3q+IxK1NUOcytkyGPFooEskYN6GRLSB86Ijz76WDr++P7p4YcfCvKbO3dOLNKuPNRVuz/wwAOx0Fn49fmAAQPSE088HorUlSs/jLQffPCBqAsSY9KljkuWvB9lstggdHYD+ko/DRgwMM7bsmVrlOWpp55MTzzxROrWrWss+mBxFjgg4SlTpjQ4Mb700oux6CK5Bx98MAjQ91Su+oQq9r777o00kKUFQ5BhUTBW9Zn8HaP+2thCbD6AcfnKK6/EWDP21LtHj56RhrJR5spHHR9++OHoxzvuuD0Wu0ceeTgWA8TNdsGipe9OPvnkNHXqM2nq1KnxfRVcTKuLLcQvA82aqEEQ3aNdq9StXavU+DnO3bp1jwgLURmAxsju3btilQf/F5mJoEQGjkMSLhcdXwtEyDcE8fnLICE/5ZRTg3yBCZToSNRjsLM0veWWb6e3356dpk17NiaeKDQ75okETzvt9LRnz96Idk44YWiQn8tGiwNyNrFEKvxKlFlkNWjQoIioNm3aGOQk8kXmJjqIrIYNOyEiM8jR6eTJFwQpZ5jYOV+kLeoRIU2Z8mTatm171ANGjToxDRs2PK4ukPnHH6+L75U7G1Cpi4iXt4o2zpOya9cuQRjaF8nbRtLmJjriUhdlFVErh3OVhZRfdIc0kaU+atWqZRCyeuoj5/tMO3tlRcsp0RWNvpSfKB62b98RBCpdfi7aX3RrQZCmOlmAGHC5ctKupP2IacOGjWnoUGkPjOhbVE/+j8hGjhwVnw8fPiIcFI2fZ599Lsom7ZzG+vXrwsrA36ZNm9PVV18di6mIFizs2sD30tAW/q89LWQsCYy3UaNGpQ4d2gfBIukhQ4Y2tLexYkHJVxCARLWLvtIu6jF48KBIF8aMOTkWW2NNPbQLDxppyMMCbszLA2kbCxZNVx1MzVyluOrbsWNnELI+c1UJzh86NNd5Y2quaNZE3apFi/S/TuyfHr56RLp0QJfUstHWh0tK0c7PfvbT9Pzzz6cTTxwVEQkSE4X17n1cXL7bRhBRITBRjEnpUjDDexNARCaqYeKUISIUXVx++eUxiUzKiiBHRzQomjAJ5Hf66adFZCkyMaBNBKQoApLHySefkh599NEwcDIpfGZycvb7z//5P6VVq1bHBENK0rjzzjvT9ddfH0SnXMjMhDbREK6FxSQzqZCCCNk2TeVVMqihDjlfl9zKhHzat28XxHr88QOClERvLsVZxCI5EbYJCCLpyy+/Iv30pz8NEnMZLuKzKCnLhRdeFOVwdSEdbeN3A8Qpsq6Mr9rGq7a1wGkffWXrxvHZsApJSHvZsmXR3qNHn5iWLFka5G9htmcuOhw7dpxlPEgeUYEFVNlvv/32fVstg2ILZ/r0V2PRsF1iu0odEBAgLIvZmWeeke67774o//jx48PTJROathcZe7WY5st7i5w6DRw4IMpk0RRlivDPPvvsiFKNN2MDxo8fF9899NBDkYerLecZx8aqY+VrAfGKYG3tuaKSnvL4Tl1ceWlH43bRooXp1ltvjSj3nHPOjfT1pa005wgcXEUOGjSwIbrXtq5eqkV3R3zuOGMh5++Vza/+tyB27twp6qKv8jG2saZMmRJXeLaEmqudb7P2+shVP1Dn57skTHoD1nEmtAFvIPneq++9+nO8v3wM+L/3+dzsuocUDObf//3fj0tmEWvllNci3ud88x0ljvFeOnkPtxZVhLczBn5tXvnOjZyWz/JdGz7L+eZy5rrlNPIERv5eTbqcju9yG+VJmtskv8/H+Mv5+ZPeX//1X6fLL78sXXDBhfF/aUjX9zn93A+5DuBY9awtd+0rUkQ+iCGXN/dHbR/m/0srt7v8/N/VkyiycV+Kcn/729vS7/3e7zWMgVzP2jrkeue+adwnuZ3zWMt1zeXI7ZfHjrprV9/LI7djHpvOzWMx93E+tqk28tpUGrmvfG+rzyJ84403NrRRbbr5HER+6aWXRjSc65Pb1fGN50zjcjQ1r/J3Oxvl1xzRrIn6y0Z+eosftZrjADQRXa67IjBJjxUMaZGw6OxYplubvghQhPh17zd9lJ8+dLCAxtVPcx3HXwQKURcUFBTUOZr1HnVBQUHBVwGFqAsKCgrqHIWoCwoKCuochagLCgoK6hyFqA8DfvV2b2xTv3YTAORb0Wo//yw30xBiSPdY3pDjLoVD+Xo09nuoNVL6os2F5EeYczT5Vf2yNm7nOxxk5dvB0su3rWkDxxOZuHXtcPJwbiWYqtIhnT6U94vjpX8kbVUrpDoQ5EvCfzjeM7Xlbvz5wcamceYukKbOPZw8iVy+iiZWnycKUR8EeXJSTpF2N3afg8ceezzUibXfESIgitp7Ur3P9+g2zqNxulRaJLq1ZWh8TG26Bzqm9nxCi1mzZn2qHE3lb5LxgMjf5+Pd871hw/qDlqX2fVNteajy135H1u2e5VrzrKbyaep89/669a+2/NV3n+4DIpX8lPim8kKspN0WbAISsu9nn50WbUryfDjl4ldiMfBHCt5UO9S2Eyk/0UnjujV1HtKtxuenx0vjsUdOzmDMAnOgstb2mXv9s2oxf0/kkwOJptLgY2KsyCs7Lx6qr2v7595772sg+abGUnNEszZlOhRMUL4NiJjJkgHKIc/9otdcc02DMMNA5pNB4kpVx/mLjwMpMd8CijIRORk0wyFqugx+EaTK0rj66mtikDuPjwW5LXWZSKlTp85BoGPHjg2ZNTUkmTQTJWWkcKNoRMbEC8xwKNwQyaOPPhJ1YfyEMORJhUglaBIgxOwPcsUVV4QSzEIjspk1681Q8fmOxwXV5f33PxATkFJSOqTtJthVV10dvhBEJtSE+R5mRII4tQGlGS8LbaHtbr755vCz8H/KPm1Mrn7RRReF+u+OO+4MxSPl5uOPPxHSb+KYd955O4795je/FXV2zqWXXhLqyzffnBXEq60JX0ivTzppTPhuIB7qTvL5fM+vcvDV4J2hTfQZAyJtTC6uDaX5gx/8MMjaQswPhIcJ4n388ccioqXaQ+IUnJSW6kT6zyJAufXxY489Fosdtak2V07tc8stt8R92cYaT41Kcj40vldmku9rr70uBCTaS5+Qtt9wwzeiT1euXBHlpHT1aswx8pK3fBA/ZSE/EGPTeKBy3L17T/T59OmvhLhEubPSMQt4pMdnQzlZDRgvlI7XXXd9tFFl5nV5+HJoD0pWabEIYJ3AgVI6xscnxlEdo4951JDeU8jyFmHexUtGVG5cU/OeeeZZacyYMc36Hu0SUR8EzIHGjRsb5GJRR3jIU4QtWsuozHnmBoG/8casIAVeHNkghxybN4XBafLUYt06go8eaeLEM2KgLl68qCGiQgrIkmuaiUnGLdJl3kSlhZzk7RiTjcw9E7QIEBA+Ahk3bnxMEuY8pOUkwCYeohZRWniYFSFTvg7IS52QkkkizeXLVwRBWZCuu+669Prrb4TBD08JE5K8fPXqNeGVkZV8IH0LEAm2iJQtKlI28ZSfqRSiQvo8HSw0iIy5k3z4g3ChQxgWJw51FkTubuTOQ4YMjnNI3tX3W9+6MaTr2lBEftNNN0d0x7dFnhaSWujLyuFvUdSb7Nxn2oJ3BVI/44wzIy+WAtqDnJk/CyMl9fv2t28JglJ+fcP0CYHeeONNIWHXPtoT0SqPxcCCxYLAmOI6CCJRMvfzzjs3fGgsfMaV8y0GYCyQiDM94kYnT9J/Ef777y+JBUo+PXv2Cpm3dmU1YOwZm9oeYVfjYlyMG/kzXsrqUjA25Ms3hHeJRUw/jhlzUni+GK++Q9Ty1q/GaHYLNJYttupnDmkT9TEO9LkFK/uMWOD0n4WbbF56XvNi0NxRiPpgjdOS+9jmffvTe4M4Bgw4PqI4arqM1q1bhVcDcyS2oiJJkYDByvkMuYkcHEOSbMJ94i+c/R7aN+zLIUzEAQY9kmRMlD0gkBKp7lVXXRULgPOrKKhFRN2c9nJUVNVhU5ArkpKeSO2mm26KyQyiIGVkMMS+lCsfcmA05HOTxoTPEN1LH5EgwLVrP46IG2lfc83V4VRnEWHSA9pDGU899dSIjJGt9J2TL921MeKypeDYLBn3uWhXflwMTXjScJEpDwuRPx8I59Ru3Vjkqn7YFv9XVxElIyTlswefzbVqoW+U22LmqsDVEWQLz1pZd/Re+KpU5bcwID1+LcyZ1CH7Mlvo87jwmT70f6Slbq5IclvxQRFRO4d3Cd8MVrvZP6ZKd8O+veCNEe1W9SfRbxOeM5WDXWVpy7HO2NM2WV7OgEq5lEUZrr/+hlhYELaFoPF2gzoZf9kh0vjVjxYD9ruu9IytSq1Ze2aLONeiqtz5yuwb3/hG1JFHjrGQt2eUpzLVahX1U8927drHFcDh/sbydUQh6oNA5OEylqcvNy+XZwsXLooJmWFLhLkN8yPRLoIQpbiEY2rDacxARRBI2nGVe1wVkdvGEBEx/pEHcpgx47WwVzX5RMIGN5MgE9qEQHbIUWSPgHxnAoqKWUWaCHl7RWSvvCYRtzLbKyIrRJovJb2IdCdOnBCX/CJHC5GymZwibLacJ51UmRuddtppDWVyaW3SitREY7Nnvx2kPm/e3IgggXkRktQ+jkXYyiNSFe25tH3mmWciTRGWScoACynbapKPejDwEZW55De5s92q9qvOOTEu3S0e+kUkayGzVeCqxJXHBx8sT2effU5sxVR2qwhiVxCXSLS6ihoXBGcbQWQrfe3reC6Eomx/2oUTnejeNoI6qSsSFhVfeeUVsYhYTCZMGL/PHOq42MqwjaH+ok39no2fpJXdAxlGXXedq6mlMe4aJm3LFtG2toTUxcKrfFz4BAv6TN/5XJvqLz9MasdRo0ZGpHrJJZeG2ZKo1jh3BWPMGE/Gp3YxPhg9Zd9v49GWinS0vasBedneQcK2lPSDqxlt5v+uMpTDmJKGersCMT98ZzFRHtt22l/fubp0xWC8z5z5eoxrZlN54W+OKBLyg+BgP2JkkjucHzqycQ2IGlyumrS1pjyfFbV5HG75smmQLR0EV3vZC9Xe+30RNZqsyPJwgUgQGaI4VDk+r/odCEjYnQ/6QDR/1113pYsuurDhQQ9fFhrX8UDfI38LH/I73HMON19XGk1tD32Z2LNnT2zBWdhqt9SaEwpRf8HIE6JefhjJ2wXK07hMtXciNPX9wdKspzoerHwHq389oqnF6vNIv57aYm8dlumLRiHqgoKCgjpH87yOKCgoKPgKoRB1QUFBQZ2jEHVBQUFBnaMQdUFBQUGdoxD1EfzyXPtk7HqD2/4Ox2inoKDgq4fi9XGI+zeztNc9qz//+c/SlVdeFWIHKjqiEDfr1942hMiJCyjciC1ImIksCBAIIQgMqB19R1zgeDfyk+L6jNcDMYz3FgbHE8x4n58sTjnmlQCBrFo5pUG0QPHlPmH3wxKTyIPAQP4UaAUFBV89FKI+CEinmfGQVp9yyinhFcE1jC8BeXO/fn1DVVcLxHvnnXemkSNHhOrs7rvvDpXdE088Ht4KFGzEBB9+ODKde+65QdC3335bKPuoGhk0UTNyQ0PsDIUITX75y1+GqY3vqQMZJ/n8lVdeTsOGDW8wz6G+e/DBh0LMQe2XFwsqr0LUBQVfTZStj4OARJvkmTRY9CqSJqEVTTOoySRdaz8puibZpaTinkbuSxbOr4CPAdktQ58shxV5i3Y5iSFbpOs9wpUeqW/v3n1C5XfmmWfss9vcEk5wTISkR3pLnjtixPBIU6QvDZE1WbA8RNi19qcFBQVfHZSI+iDg18BlTgTMo0GEy8sXYXMBIzmuPBZGho8FC0jeBwgSMfOkEHmTaCNsBCpC5ofQpk3V9LYwEP8jjzwcrnOk14iY54UdFccif+kxweHFwaiHpNu2y5NPPhVErKxc33g9WATkKT/71o7lbMZYSFn9v6Cg4KuDokw8COz9Vk/O2BlRL6JjzIMgRcu2NvJx7EO56iFWJjyOzedw/kL2SN2Pfjw1pMlpTIRrX1rk7nhbIfLgmJY9orMhkK0L58nbuQxtlA+R20NntIScHcM+le+w98qDnEXsFpLsmldQUPDVQCHqYwBRsz+k2pz9CAoKCj4fFKIuKCgoqHOUHxMLCgoK6hyFqAsKCgrqHIWoCwoKCuochagLCgoK6hyFqA+CWiHLwYQih3tMfur3FwH5uOXP3She84NZDwa3Gbol8EjLmH1Q8sN5D3WssuT2kp9bCA91TlN9kd9LTz2PVNCjvIfK+/PEgcZEU/U9nDEG2uFw+rre8UXPl3pHsybqQw1+9yv/7Gc/S3fccUfcC30gGFCeY3cwEL/kJ1ofLqgJly9fno4GCxcuTH/5l/82TZ8+PerQ1BO3G8M92f/lv/yXI57oCO9v//Zvo70OBcT8ox/9XdwXjlR+8YtfRL4Hw+uvz4z2109Tp06NuvBgke+0aVPTO++8E/X0FG1P1j5ceOK6p8MfKyifNjictgZt4JmUjaFeTz75ZPTDm2++GU/q9hlLgEP1DYEWVWw9QzvxqzlYXdTXsyy/zIW0ntCslYm79qb02/lr00UDuqSBndp86h5oCj8Ckt69j0svvvhCOu+888ODw9Owu3btFt8BcYknQiNVk8pTmsnFPYmb5NxTpj2BnMGSiTxz5sxIw9OWgSzck7IHDhwQT7n2RHBlmT37rTBo+hf/4l/E07eZO5GLIzhPsq6eQD42HpbbtWuXUE5SNnpyOdJCipVyckSQ/rRp00JhecUVV8RkeeqpJ9MFF1yYXn75pTRhwsRI11PXTaJnn50Wsvmrr746tWnTNhaiVas+DNk8DxQKSw/EVWfkoK6bN2+Kp5Uj1csuuzzyd56nicP06a+E30l+4jaSlY46Kh/yJc654YYbGsRE4Anc3bpVBljyVq677roz/cEf/GHI+y2U1Jw9e/aKdn/++ecbnr6tvzyxnK+KtlCH116bGU/Alg6DLFDWF154PurqKeTa19PJ9bk2NRY85XzBgvdC0i8tT3fX/t5rA74q6k/Wr131Y+M0zj///BAd+U5ZLFKIXX8hp/POOy/KzkjLd8qhfoMHDwnLgLPO8sT2p9Pu3XsiLb4zH3+8Np166mnxxHYKVMeA46WzZcvmKI+nji9YsDDaRZmVicqWUMoxkydfEEIq9gUWPmPcU9FffPHFEFvxj1Ge9u3bxTh4/fXXw1/GU8aNReNcHoRWxm5KlRDLg4R55Rifyiute++9J/qZ0lYf9ezZI+aDcW/hFWioCzKfPn16tN3ZZ58dIq/miGYdUe/Zuzf9/Ttr0kUPzEu/nr827d6zt8lozqPqDW6Dq127thHVrVixPL3wwgvxeHuTHfmafCZ6586dwgSpT5++ITWvjdgpFwcNGhhpMmEySUWYBvkjjzwSJIOEEAliomicO3deevZZRD4w/ehHP0q33vqb1KFD+xjgiJL83OQA8vLf/ObXsYg4V4Q5axZjqR0hV7/nnruDFE3UF154MQjigQceSK1afTIUEAPjp4ceeijk7cr/0EMPhiugiYbgLRoinh//+B9D9egc5EHqjmDuv//+9Pd///dp8OBBMdn+4R/+Po0cOSrI6PnnnwunP/Xle/L3f/+jWESkjfCR3sGgHuT7lJzIqdpKSemtt96MiY7kV65cEXXSZhYi6k/1YJLVqVPHMLVSnwx5WqiQjv5FlkgLKaqveqjXe+8tiPSV3QIgIn7ggfvTmDFjwn9FP6insWGsdOrUOT399FORxqBBgyJdZUNkJ5wwrGHRFd1/8MGy6CsYPfrE9Pbbs1Pbtm1izCCv4cOHRzt16NAx+l+EXZVtcLSpxV/75+EmDwS8a9fu9NprM2JRM6YYhIm6LZD6Tnuqm/zAU+fZGVi4kOy7774Tbf3UU0/FYjx79tvR7o5XH2PMImw8z5jxWowvC/SoUaMiT4HOY489FgGJRSHbGPC1sbDpJ+3lOwumqwlPWWeGtnjx4mgf40Zdm+tWSLMmamjdMqURXdulYV2pCj/9vUhTVCFy2LBhY8i/RSc8Nd5/f3FatuyDMEwC0S4JuEGHiBHBjh37X7qJDpYuXRYT1ODzaoBLQ6TB6Y7Px/jx44NoTSwk2KNH9zRkyJB47xwT+rrrro2JIipx/oMPPhiTxuWiySfyM1FAxP7ee/ODTJ1v8l944QXpZz/7aSwSCCWDI5/JWe3h7qraqXXrNGnS5HiPGE1C0aS8vO/cuUtMtMcffyy+ty+OJDgBWjS2b98RZRQ5waZNG4MIEaMy7d27J51++mlBXraJTNh8aayO2jX7gauTKxiRaWM4B4FwGhR9Iwef5X1xEbe6XHzxRfudr68sTggEGSEm75VDZKlcznN1YhHetauyADAW1K0ivD6xCIjUN27cEKRdmwaDLGls3botrGvZBFjY1c3nJ510UhxjseHdgqCMAYsN10QRvasyZRMESNeioWzS7NevfxyfxzHC9ZmrKMEEXxjbLcaQhVp6+snVYq2fubGFoH3mXBG6hReZi5zNB/lYrH1mkQJjQd+BMjvXPNi40Zjcsa9eoyIqttgY7+vXb0itW7dJ558/KdpNGq1bt4q2EfAob4sWKfXt26fhCrQ5olkTdesWLdLfnDUg3X3F8HRu306pZSOmNhFNtHPPPS+tXr0miNjlpO0LZDF8+IiIckxcEbAtCORkwtuKsAXgc0CMokATi1Wqge4z6Vx++eXpH/+xikzHj5+Qhg07IQY1Ap43b37YqZpEIunKb3pXEOPjjz8eE060Jy0T2MA2GRC8ySK/QYMGR94mTC6v9OVlsrs0z4Q4YsTImNwmMBLNRC/68d4kVc45c96NS2Augr/85S9iYskTATGP4uR3xRVXpp/+9J+iXRznykF9lO+UU06NiflP//STdN1110W5bF1Ix9aIKDjvXSMKEe+tt94a750H77zzdjr++AHxf5MfMWrzdeuqxRIRIzbfiUy1FWJFQhaVHO2BhdEVisVOu9iWef75artrwIDKfEsE3b17j7icR6TcFbWfc2+77bcRtVqYRJMWeNtHrpBq0wBjQvuKMo2Ds846M23evCUtWrS4YexpS3+2G0499dQgNYvv2WefE1G+9jnvvHOj/tK1vcSIS2RubEDe17bPrYzKb2tJ/trE//Vh/p3G9hIYu65A8lbSe+/Z7ukd4//DD1fFVZbFXpsiVVeTY8acHFcXonfzxlg0xrp06RzbFvK0YEvb59pD20hTwOMKNbexc++8845Y3ETf3bt3jysZeTZXi4ZmLSHPVT9Q59dal+ZXg8WA8ld7fn6fTZf830RxXE6/No3sipfPrSKJ1vH/fB441uc+y39/8zd/EwMe8f7Lf/kvg3DyeTmPnOeByu7/yOq//tf/mv7qr/6qwSAq5117LNSWyXv1zA8+8D4f633OP+eZ2yO3TU6rcTqNUds+ue7Oz2VoXMfG/Zrr0bitG/dhfs1lr61H/q5xH+bjc9q5nnkM+LypNGrrk5LPWu7Xdzm92r5o3P4HKlvuh5yPrQrbLaJ06TZOs/a8nF5tG9XWLb/PVzkWBAuNRQvpX3TRxfsdV9tOTeWZP6/aCQF/MtYaz529NWOnEHXBV+q2JREZckVyRzt4TSwRoXSa6wT4OsNWESLMC8ixHoeu3vbs2R0RfCbZgs8HzTqiLigoKPgqoCyDBQUFBXWOQtQFBQUFdY5C1AUFBQV1jkLUBQUFBXWOQtRfMPx2675id1wcCdxKl59cnuHuj6y4awz3v5KCH055pHuk5TmWOByzIbdrqVO+64VI6FBwS5e2PlDah2t0dKBzqeYal+Nw0nM3Ru43f+7Nd/fNwepBuNP4FkR1a+yXQRXoXunPC4dqsyNpT0IftgEZ2iSLmtzr7q6SggqFqA8CAybLvBGZe52z41pWFVa3KFWucdX90NWtc9nNDalQutUeQ7BAuZbdweThr/bWu1qHOP+ntjN4pUN9KH2vvBxqP8uOdD6fOfO1+C6r0Wrv9a7KtXnfpHgv/p8/yyo+73Nd1Dnf7+3/2qS2zOpYW+ZcpqwKdLy/TLI+yypKk5VyMqeby1ULQqK7774r3vOoIH2vlHDbGia397Vt53vy7dp+zPcIy8PxZPLKVFs/ZW9czty24Hv5KDOycU4eB5SnuZ9q28gx3nuVXpbmS4cwxiJU2yefjKedoZp88skpDWnmOlL9ZTVhTou4xQLSuNx5XGWnwTx+a9vQax4nuU75fT7GX26z3Mf5HH8UvGTu+dbPXNba+nv1PfsDFg257ZYtWxrtIA9eH249zWNi21G4On6d0KxNmQ4Fpjt8NBgqMeoReTLNefXV6emkk8aEv0LlizAwTZo0KQYVfwtqQPeuMjS68867YsDxY7jooosaIgeSXIPWk8sp2QzwG264Pj3xxJRQ51188cVhYsPjgXyYIoyZEpKnvsumPM6rIi5R+u5QAN53370hayY551vBQ4Ly7Xd+53dCxYdIHn744Ujf8SJv6kXKxx49+FX0CZUf9aFJf/PNN6f589+L/Cjd3Hedo8nvfOc7oUwz6caNGxuqPhPq4YcfSlu2bA31GrUe/wbGSBSDo0aNDBEGwlUuUmOydeIMHiBUatSg/B4yMhEDgYS2u/fee4PAyaO/+93vhjfF8uUr0mWXXRZKxwzRKB8W5+iTJ554IspFrcnzQ3szE7r66mvCB4Mx1u23357Gjx8X7eE7/awdMkFaCOfOnRNtctttt+1TiA4OdaP20f9Tpz4Tvhu53ShCLeSXXnpZqFuJRrQbjxMmVtrI/ydOnBDGXsr761//KvJes+aj6HfqQ9A+tW3DtIvvjPpfe+214dSY7Qb8n8eJcUkVanGh+NPW3pPzU4FSD2rLW265pcH/xTg3Xqp23pQuvPCihjbjFaIevvvd3/1e1Jtsn6qTupCfiL6s2nxKtPmqVatD2aq9qCWnT6/8aihVjSfScopb9/VbjBH9smXL4vX73/9+MWUq+DSY/JAcc+0yGA1qA1+EIyJy6Ul2zeUrTxiOYTff/O308cfr0jvvvBsybBPcAM4RATnwjTfe1BDdkc1K3yBu2fITVR+88cYbQSAmO0Imu0WwJq3oQ2TufAN84cIFMWHPOOPMdNVVV4byTb7KQ7KLXEGEgiwvueSSiFwsHEiB34ey8iJBRFRnlV9Ip6i7/LndiZ4RPI8QWzLqQ1jBmQ1MOMSobSwkyjlhwvgwtqKivO6662PB8J58GGmbtBY8aWnDxYsXNeoNCrXqnYVT+4jEeHqQGCMbwgsknvsjw4RXdnW0+GjaW275TsjR+WtYhPWXqC5ffpN2I0/l1LauOrI6UhoWKCTmakC7M2BCONKbNOn8IDdSdmSHaBD0t771rZD/aw/5GD/f+c53wypAfahNHa9PgNUAQkOcvF5cVSHHli1bfWpbyzi59trrYjHUFsaHxYt3jCurIUMGR99aLHyfI3Xj7qqrrgoC5IaoH/m8iG75kCxZ8n5DO2fZuTqStTvu29/+dowR8wH4h2hPQQL5uMVCG3kVlBi73/zmN2McGfvGhc8ZSFXbdUtjAc8OghaHiy66KJ1wwtAYa80VZevjIOB9IFIV8SAHjmXem0gMYxAZ+0ZkniGSyvJkEYyBbkCbaJl8eWGYECacQWkA8jVA3NzkmCdlnwtEITLhtyBNZjwi1CuvvCK8LJwjEjvuuF5hbtO9e7cgjnffrTyJTQzni3x4R2SYhMqAzJGgorVpk8veIm3fvi2IxMTjB2Hiu6zetm17RHiOdT7CqaxfPzEDcgVh8oraTH7/5+qm/iagPJ5++ploX+XQZtJH2m++OWuf/0aLiPiyw53zkI4yWIwGDBgQpOZc5WAQxHlPn+yPFlE2UZzFhUfH2rUfR5+4QhKla3/pyM8ipZzqDLYc+Fzos0itRYuGKx0EhLh4k7AZdVWlrkhUv+kTdqAWqNznlUthJVvXX9IRdWpHznPKqszQvn2H+JyniUiX94a8+HpIs1ZMmseJBYKDonr5v0VHdIv01I+SUFtpQ4uaciibcldt2TLSkhfTLba0te2s/tLWXz6TB2tcJO47n2lX9eJdkm1JeYL4LrsBZiUjC1vlcuWiPqJy7WZe8QGp2ql1lLE5b30UZeJBYJCzc/QDjQl5xhlnBFFwgONYJlo0WRGSS8H8wxCXOK+iCgOWMZDLaWkAovedyI+Bk0mDdF06+86g5s5nQItmbC0gfRGnPERWCMm4RabKJS2DXXT6xhuvBzGa9AhOtMJoyWWvySQ65nImgkaE6onI1NP2gy0f+6a8tBkNXX/9Dfui7h0xgRGV8oh2pJ/3RLUBAs0G+oiTaZAFwmciX4ShfXxvQWFyJDLnbexzkZkJO2DAwKg7ksk+JJUV59th56lsjpWnciME5GtS28ZBrsqkbMok4pSuflAG59iqWbJE5Ngm6mMrQ/20gchVf2gLkbI6WLQASVmw9YH2lp76+N5Vgv7MPuHqIPp0jqsr6TlP28kX0fm/unsvYrcF4U+dLeT6S/6OcYUkIrYw5DSrcbIpzZz5erSxcSJ9VwFMtrSRMulnBK8NnZPbSBsby/I0nr3qG+3tqkEdcjs7x9i2UBtvyqN99WH2BTFnlEH/GCvGuvHjVf2VWb/Y8nLlxJVSFK6elUXtyiiHNLVBjx49YpyaP9UVV/NDIerDgEFs4CPkekF2RkMOJsqx9OowORCRvV0T3+U0UikoKPhyUIj6GLjsfR3LVXuZWW/1LihobihEXVBQUFDnKD8mFhQUFNQ5ClEXFBQU1DkKURcUFBTUOYoy8QvE4fxAd6Q/4pUf/b4cHEm7f536qHFdPusP2l+ntvk8USLqgyD7FRwruGeVOOVghjbuUXWf65GALDsLQzIIVg5mtFR5Q3zaU+OzovLV2NLgbXKsQEnnXuhazxT38Mrry4BxQZ5/OKC4oyg9klsj3Q56uMezCDCu3LdMoOWe5WMN+bjP273UtfUmUDoc869a0Aq4BzyDH0v2LCloGiWiPsjAnDVrVpAD6S2xwIcfroyncROn5GjCjfnEEbwl3LBPPEDpRhBAdJKJhVeB88mPKdio8KitsvjDxCRQMdGIDGrLIV0KR+IR4oictzQNegIY93iTblOFSfOBB+4PMY283AduIhBnEC8QaFCfedIzCbP6mGwECYQQhA6EG+qSFxj5+I4IRD14dLjHWtmIID76aE2Q6eDBQ9LPf/6zULQpE0EDwQLBDdWcvIghaqMndZE/WTGBhraQbn4gKxFLLos6WswGDRq479zqQau8MAgkEGiWu6uvtIgs5OlecOcSXGThRBbnqLPj8/Mjs8EQkvWU98WL3w9JtD4ggNHH+ur000+POmlf5ZQfgRPZPZGItl616sNoP09w96rd9COyUlblpsCjPs1KPuNGe0nX9xzxSMq1T+097Y676667wj8kWxjUQjvwKqGC1b+VnHt7jOOsDpS2hV5eeRwjfX2tboQz6gJZhq/PCYO0HYWl83M7q19t/woYELx54pz8nf/rd4GJp6s7xphW39oHBS9atDBe1b1S0zY/FKI+CESlJqqBi/hGjhyV3nprdvg8gMFj0hr8JiSfAh4e999/XxoyZGio/RAHcqQOHDPm5PCU8P+PPlobHgx8PH75y1/EU5xNOn4VTJX+2T/75w2D1WRYv35DuLX9+Z//s4aBzminf3/y4KUxiQlg5EthR3LMo4KyjVKMghCZUlaefPIpUa88Odet+zgmKyk4Hwc+HTfddGNDO3CEUz++Igj49ttvC2n4iy++EKRBQo+4qDDlS2mWFw3vmSBdeeVVYS5VK7fPoFITEfKjMGnJ1KnQLEAUa4Q3P/jBD6McWbmI6KgZvdcupOGc2BAkMpH39dczuXo8Jj7DJipIqtJ+/fo2EPWaNavTvffeEwZQt9/+avr93//9BoXdT37y4zRp0uQgJkSnDS655OIwGMqLHh8R6VsspE/RqSxUkvJ//PEnYpExDrSxNlSmSy65NAyWnEtCzgNE/xsziJGEXZvKO+fDm0S5aom6MoMaEn3B60ObUB8iS6AsJdOWBrUkIzHS7Nongxs3ApFqkdkQBlnanSEUQZWxwuKAunHMmDFBrkhVWshfmYyJ3M5/9Ed/vN8DdZ2vLc8/f1JcWVgoibT02+jRo9PKlR/Ggq6O8rGA8bYB7bZ69eqYd9YhxzdHlK2PA8BkFQUaiGCwkFDnyA4MUJPK4PbHXOexxx6N80TU2VIS0RuAottevXrGwBVZITUTSCTNlc/EFMUx8+HJkGEBMDFc0uYI3asIFJGQhq9evSY8LPbuNdn7peOPryTj0rPFIULOtpGUhqI7UZKoSbSIxBG2qIwJFRl2hsiO5JyMmSTYeeecc05cMXBTE3WJBtUH8fte3iacsmsb5/KpcKmcbVQ/qd+yNHr0iVEu54iazjvv3JjQIi1SZUSiTcaOPT0mtuhNG3CL433iXO0M55xzdhCptJCrKI0kXZ3V1eKa8+dKOHDgoFiAsi1rBokzwpCX+iN4Cy2S8TkJfmXpWvWz9xZH/Yy0LaAjRgyPY5khGUM+qxbI7bFw8+3QrxZ7bTpx4sR920fbGtpU/51xxsRY2JVRvWvbz4Igj9/+9ta0a1dl19q470Tq/izEPEVqtxqMdeU49dRTwmdGHbS7Kz6Rt/GarVulzT5BHfVJZZm7fb92bsqTOjtMIt5smyq9yZMn75ObL2uwJHC1lyHN9957LyTyytFcUYj6IKjsPBfFwELQBq5LvgyfI1fkZSK6DH755ZcjcmGh6f2MGa/GhOfP8OCDD4b96Mcfr41o3aAUeSBRk8VeI5Lq2LHDfpewy5Z9EMTvuIaOa9kyynTfffdFlIk8kCGTI6ZGiEKEjIhdmiJTZVJOFpj2QVml2t6RLxI3Qb2qT+2lK68KplSi3Ox6VxkXtY/yVi5+G8NBjukOFzh1r/YuW0TeIkmviE6etfvwSNVnDz74UBCJ40SwTInUkxlW9dohFiQGVxYW+Q8dOiS2INhyiui0nX7StvrPgoEz9IFL7W7duu73AAbtZhuK7SyHthwJqp/28KpfETETqexH8uijj8ZxfDHY1L7xxqw4N2+LKbPLfPu6rigYG61cuSLOcYw+UUbtZ8FD6MbHU089Fe1v790WijZlgmVB96oOLGrzgq1cDz30YES7tmW0We32AHMjpMlETN/oR8RdS9TInbUoAp8wYWKMTe3hqkM5jCWLXh6vFjpjSEBgLFiwatvZlZWroVrMnTsvznEF6BzlcdXg6lGAoO/0p79a8zCL2qZNm8NoqjnbGBRl4kFggLtcRVCiJpGuqMSAzfuYJhPCM/lMml/+8pfpT//0T4NYnJtJPEcReTDm7wxakya/igqrwf/JXqnzRBvyqP1cfiau433nOISpjCar7wz6ypR+exgOiYBEcD7PZuwmn8ilIpDWDQ5xte1gcUEOolvnydOr46q6fhQE6XPHW5i6dOkaeUk311lb8isWwdlDz+nnbRhbGtLVxiK22lefy0ubq4s2lS4Ss9XiXGnkK4e8x53zVS/t6LhMyPkBCf64tmWXvPwbQK4PcpdeZalaRd7KJH/f5X6Vf+4L///E/Y9LYJvoE987V3sqq3O0o3Ry//p/3qd3vLJU7d8jyprrU7X1pvhMvfLWSG395Km8ztUfvrfg5sXYYm+xtLCoX97nz2ZaeRxJQ7q+197S81ltOyu/LS5WuzmocT6Svu666/Y7x6uxqX7a2ZhxJVHbP+q8du3afYFEh/3GZXNCIepjiDzAK8vPcqtRU8hPG2kctRd8eRAtI34Ly+fRvyJ2P/bafy99fnQoRF1QUFBQ5yh71AUFBQV1jkLUBQUFBXWOQtQFBQUFdY5C1AUFBQV1jkLUh4l8W9vBvDHcGlUrmPi61M9nvjvaujUlgGjqGHfMZFGF/I6V/4P03MpXW4Z8d0Ium1vADuaNcqB03Xp2uL4m1VPOK1HRkcLtcP4Ot665Hd0eeCi/mnyrZxbtHKs+PVooe+P+2rp1a9yi2FxRiPowBmO+p5Y0vPHntX9EJFn1lidK47+m0j7Y8Uf6d6D0D3WO+3CJHBqfm+E7xxyo3AfLE+lSth2qrMQP8kAYjKZqCfBQbXqw+iEq6dUeZ9EhYZam78mXm0rnYGnDvffeGyRyOG3suHvvrcZQY3XhocYDQdBbb715yLECZOQWHseRdGcCbqrtwff/8A//kH7xi1+kKVOe+FT6tf/33p/yZIXpoepypH/Krg617fTuu+/GQ56bK4rXx0FAtk115t7Piy++OFZ5ROwzg4n8lYqLqurdd+fEvagk3G74JwkmEQZ+DfwrvvWtb4Xay+B7+umnI31p8IVwn6mniZMZk6Wfd975MTGRyLnnnhcqQyoxfg68GRCa+1L5P1DJUZvx0aBEBB4f1IGVF8cFkSZZMMKiVps69ZkQHVx11dX7FI9VJCZCpFJz7+tll13eIJn3f2WRN0kzvwhyaKpATwQnzzahKOvImfmLUDQSViDEV1+dnv7gD/6w4SnapO9UgaBuJuKCBQuDNORBQEO6TfEGiJSCUlvw/iAZ5xtx0UUXxfciR+0gSr7kkktCGWeBoHC84ooros0ZbHkyur644YYboq+Qp/MIL+RHTclbBb75zW/G/cDSfPjhh8KHRT2nTJnSkA/xDa8QKsbLL788PfTQQ9GeWaVKKcnnRDraRxuzD9AexoL2J+KhcNXuLAuIQig3SfF5bfjcuGAzgKyMJ+ZJvGcee+yxOJ78e9q0aaE+7dSpY5Ao0mUhoG0pJWfP5qOyLerlad8Z2sbx3/72t9OPfvSjqCfZNkk7Eyv3WZO2u+JhkMQq4ac//af0rW/dGOIU45RC0/jLT7nXRup76aWXRj+1bt0q5gMfEUGPcU9taDxqr0svvSQ9+eRT8Z15oG/0F5XjkCFDQiD0Vb1aPRYoEfVBQGU1fPiwIDmXrECqy00tGq9li30eB5Wjm0nBS2Lq1GmhumPQ43teEga0CQpI3nsTxsQh3aUIQ/ALFy5Ip512eqjxkAFi9t2wYSeEkRMXPeVBPggegSFTk+Spp56M9E0QRHzVVVcFoSOs+fPfCxKcN29e2EqSEvO5yGXKQKonnTQ6pMQmfm2kJD8GSsj/lVemR768HV566cX43iS1YCBjRJkl7yeeOCqNGzc+FHSInbAiR7AWgJdffimIUz2RJOKu2u/ZhryRc58+fYPwpFHVZW5D+ZSBug65WATVk7cEAib51mZeTXrGTOo9b978IAP5XnnllWEMhaCQcTaVAn3Ei4JcHfkiybPPPisWFIsdgyW+K9JH9MiJ8RKStvhk4s9Aksoq77wtQWKPxBEiSThV3/Tpr6Qnn5wSxEVaD8RUxpP2Y4pk0VXPadOejcWL4tMCyrMD+X/727dEVG1cIn5/vmsMxzL5Yk7FYMoCoZ9ZIJDs6wvn8WtRdsZTykUyzpPl5ZdfaSBS5Itw1Ym3i7nBLKxSdO4JAzJz5JFHHtnXRu+F4ZYAgG8JmDfaQODx8ssvBXE3ZxSiPggMEAZABixiAwRhUI0Zc1JEHgafaKd2f1P0YvB+4xvfiIks4kH6u3d/cozjTUyv2ZNC5EVmbPA7z/fIWnQlsuCNIPoRvYnGHIuokF1Wg2VUn1d5SAshIB5EyjOCXPqyyy4N74rG4OTWOL0KLSJCHjt2XBg3cRQU6WSyRNQInmcFQsxydOWsLr/3RLlPPfW0iMircn6yJ2miE7OJCFmkXnPNNQ05W3QQCPc/EbG61O7ZSqe2HaS5Y4c0Kyl5DPaWLcO3gsPeJ/31SfvpZ54qiFQfZuKx4JA/i5ZF7rX55CgZAWWJueNF8r1790nXXnttXPnUQrRJKu2YTxbCFtHv0lQOplqcGPmAyC+3jzav8q3qrByVYVVVX+9rx5jvlUvdq3HYMe3atTuIufY3AOPhpptuinbWb8ri1dgUuV999dVxhSVgsZDqp+wzYjzVKg69txhOnnxB+N6YBxYckfknbd06/E+qNrouFmHjLhs9qc+DDz4Qx8HntB3+lUEh6oMAeYrIOIiZgC7dDCTRga0O5Ge7w+WgiMfqL9oS2bhEFTkjbD4JHMIMSvB/bnFsLhGfKMbx8ho1amRMDudZCGwliKJMalGmP1sKJp9XWx+2K1h8sh7NE8VlOYP3yhu5Y6QrOhKt+845LokzUbgMVQ6XpKJNkaVthTwB1d2kY/j0xhuvB3GwS7V9w1YzQ1lNrtNPH9twrsjb5OMgaLvD/iOS9T1SEhG6GmBhybVPetoj+2TkPUo+HCJIbWexVJechza33WRBE9E6xyW39HhYKKs2RVa8rJHnaaedGnae+tElOZKyjcNTAjnpW/C9PVl9bHtDdK3/5WPRYQvLorXKp9rukpaoUPSf21hdTznl5Og37ebYbDSUo2zkyPr2zTffivroD1sW6jlw4IA415Xb6aefFu0mytVWtuZYmlrUbcXZnhB52xpBsNJGxrZG1N0VR17ojKXx48fvW2BaxnhXHtH3Kaec2tAXttOQvHZQb1cEXm0HKk/uC1s0yLh6sEPVdhz3pJmvmPhn2/ap2mh+zB19YxxYiMy9T9I+PZwL81ZZc0SRkB8EjX90yZd1S5cuiRVelFG713dYDb5vMDeVduPjDnXM4aTv8hy51br+HSyvph6vdLjlcLzJ75I2X00c7PzDSf9ojxHpIYPP6l/RuB8Op1+aOuZg5x2o/b8IHKx+R1OWg/WX3wAQteDis5S3OaIQ9REi3zEg8jBo6n3guIT8IsuZf7n/MtumHspQcOB+yQ8sKDh8FKIuKCgoqHOUpa2goKCgzlGIuqCgoKDOUYi6oKCgoM5RiPoLQlPS3YKCgoLDQSHqw0RTngaHOt6tSNlnwX2rR/IUZffKNla0HQyVkm7NAcvnc2keTIZ7pHU8WnxR+RQUfF1QiPogQCZ8C8hhCV3uuefuhoeMZnc3xJeflZgNa7wnCCEpRtS+d2M/FZ/PK3ntxv1Mf/yfyjF/RkRDepsfYJud3hzn/FrDIWIEUnEqyly2/LDY7IomX2VoXF71cq40kb2HkHrNbno5X8c7NuerrPLIZcjp5ffZ/ay2vPnhvfKTD5Vkzic/aNfx2eQom/4UFDR3FFOmg4AqasaMGeHfQM1H6UaViDgz4TAgQuCUi5RUCIzqjApu2bLKW4LzXOfOnUKE4Snec+bMCdK6+eabQ20lPR4bhDS8Gaj7Konw7nTnnXfue/L2hnTZZZelJ554IpR7RAPI8Ne//lWo70jBlVOZyat5hjB26tatayj6ECDi5xny4IMPhRx9+PARoWyjNkOWuY7UlaJvBOuPIpHJEvWavNWbkEb9qN54mjDlGT9+Qvr5z3+2T0HWIhRlVIhImFLv8ccfiyeTU8bxn6Cgk8bGjZuiDajreEpQuWln7cRQqqCguaNE1AcBOS+S5FHQpUvnMKJBVIx5CCmYyHCCQ2yIaPHiRSGz5ZJH0oy0RdWksTfddHMQqeOROzJauXJF1QktW4aKD5lyjcvYtImh0sdB6I6pIs0qas/CAXJzrno8LZAqPwyydPJhplF8MUTCyLIi68VB5vJbvbrye6CwJC8mmWbGM2bMmDAEUsfNmzeFzJlLYBaPyJcbmjQtSqTumzdXUTOl5s03fzv8Tz6x2NwTknQydXVhHCQfknFydR4TyDtbc/qjpNSGBQUFhagPCtEgcuLwxXgGqSE5ESPvDo51DH6YDvHiQGD8EmbOfC08F0SViGvFiuWRFiJEepVJTiWvBvvXPDlEm84B+ZI/b9++IzwVkDRDozPOODN8KRAgQuNhoUyIEY8iTw53Fg8EKm1eCxWxV2Y5vXr1jPNEwMrBHKcqU+V+Z29chMwlUB0RP/c78nnpVC5xH4TBkf/zflizZnVE4RYjVyH8GgYPHhLmPhzteDdY8LSNsqrPqlUMr9pG23jPYfDMM88IJzZXBRa9goKCokw8KETEzIAQla0M0SoXPdGey3IkjLBFnQyHXL5zJ3MOkrLHKlJEXtzKEBqyR8DZ1UwayBWxI3nmNKJkaSFr0bD9aqY2iP6dd94OkyMRM6JmQoRgbRWImHkjn3POubE18b3vfS88nC0mymzLgtEOzwWkiTxF4bZa/F/kr6yuJGzlIEoGUgiUcY7tFelYJNiYipD79+8XC4V20Ra//OUvg5gtFBYW5bX4iNLzglJZaS6PfNRfPhYGeamPMnqvDSyKRQZe0NxRJORfI4huEb697r59+8Xe8hfpq2BRYWpf62pXUFDw2VGIuqCgoKDOUX5MLCgoKKhzFKIuKCgoqHMUoi4oKCiocxSiLigoKKhzFGXil4DGj7o6nGMP9/ijfdLJkT4V5UjqcCzxWdvjUI/LqhfU9kct/L+pp/Z8Wf1R8MWgRNRfIEwm9waTgXvoKCXe4XhZeEgtifeRgjDFQ28Pp1yPPvpoevrppw7bW8N94QsWLEhfNNz37SneRwsPT3V/PFBzEue89daboZz8spCVo7VgD6BPcn94v2NH9bRx98g3NvgyPmofVlzw9UKJqL9AEMDcdttv4ynNJtRvf/vb9IMf/CDIh/ADiXslpiH4IH4BykZiGXJtqkNPlCaKqYQ1g0P9R4RCrEKCPnToCaGAJB4hUkEE7q0mSiGasUAQzTieyMb5L7/8UvrBD34YwheeIM4jepFH9eTuQVEWHiJk6c4hI1fmSh5OiTg4yJtAhoBF+r4nrCF+UX91kDcVI3HQ6tVrov75idzahRR/zZqPGgRClJLK4DgLhO99PnfunBDz8FOxKMlLuR0L1J5z586L/HL5HdepU+eQzDtWnmvXfpw6d96570nbc1KfPn0b2j63hzy0lSeDEywpJ/EOARCzLa/UlOqpjspCuKOe6q99KvFUlxArVW2zNp74/dJLL8V7Tx9nnKVMBFb6DLIalCfL8uXvh8AqG2MRC1VPKB8YAqL8mfIrl/e5/iXa/uqiRNSfAcjChBT9HA4oAx17yimnhoGT8xEvIyTk+cwzT4dxEw+O+++/L9KuPffOO+9Ibdq0TrfeemuaNm1qTGQEDSKsX/3ql0Git99+e5xLKi49isrf/OY3cRyDKHlKz7mPPPJw+IlQSfr/HXfcHkQhj7feeisi8myNigQeeOBB1BHkiQQffPCBqMdTTz3V4KbHpMricuutv4lyPProI3E+on3ssUdj4ZAv6Tgzqlmz3ggSA0T15JNPxTF33XVXmjlzZhg1IUzpVG58W9PTTz8d9XB1YqGgiFS+e++9t2ZRvD1Umz7TviByVd+HH344FgNy/Mp2dXe6/fbbGtpIfqD8Im/mWPPnz4v25NciX2VFhCJ8dRfp6hP9gLR//ON/DDJ//PHH4w/JP/LIo7GYOWbDho3pgQfub9jmIKvXlk8++WSTFrcWAFdiFhJ9rC21qbIopyuDu+++K8aYfNUHOdfWv+CriULUnwEmDpI93G0JEY9JhERMSJexiNf/89/ixe8HmZBgI8wMBCWKHjt2XKRz0kljwnWu8hSpQG49ceLESJvZEak48hKJjR59UkR2HTp0iIgeUZOFM1Pit3Hccb0j2vY9RaNoTETIf8T2ACIRUU6YMCE999zzDVakzufAN27cuJCFi3bluXPnroj8HY9YM9RRvZWJtFxeJ544Oogut6lIl1Pfxo0+2xtOgW3btknHHz8gnXHGGVEOJM0HhR+JCHfgwOq7Xbsqe1XtjOx5lPTp07thq6BDh47pzDPPCrLTzq4yQHnVyfEiUO6IMG/e3CBN5zvW9wMGDAxvE2V2JZCNsqq8U0SvEydOCBsB9WDe9f77yrstDR06JNpWxJv7SjTOAdFxInqvjbdCQJ76WFsrq7a3eCiDNLUp8penNpSGY/v27RNXF8U29quLQtSfAS43r7322tgGOBwgGGQp+vvFL34RESwi5McxZcoTMdkQG9c8JO1yuTqvU+SBfO66687YWli/fl0QFMLNEN3dcccdQQKIZffuPUHuiN02A7BbzbapO3dWviO2MLgD2jJAOPLo3p0HydZ9rn2VMx5yqKLmPRGpKm/eNxXRieKQQWVUJc8qL69gEVBnESuwWlVH6fgDBEOGzrMEASFWx/Tr1z8sUUXC0q4WgC1xJaBdLTKiR6+QtyNEwo7juQKflKljpON45fCKoNUdqdm6UefK0a/aMlIO7aUf1IOZFRJH3A8++GBaunRZ9JutFWZX2lr5/F95Eas8mXxVxyhH5zDKcvWyZMnSWHyUXTq5LrleiF8U74pl1KiREekjZ1shFgl9aJFw5eVqxWLkqsQYYaD18MMPlT3sryiKhPwLBlJDdiaf6FM0i1j93yQ2QX2fiQ5EXT5HDqJVJlHZZN97x9rjvP/+B9I3vvGNhgnue8SCYJzrVZSZFwD5ZKJEPvk156FMIsvs+pfPkV8m6Z/+9KcRZb7xxuux3y4yrc3TecqfiViUlz9HYrV3X+Q96rwFoAyI3zGOzQ80kLa/3I7Kl/NQv5yXYx1TW35l1ubqma9wcjly/bR7biOLmSuCakFrHeWzv2xh9VuAqP8b3/hmtLV8nSdNr8qU/y9/x+SFq7a8jvGdBQepNu4TyPXSfv7shf/sZz+Nq5HZs98KH3OfKWOuszrV1t956lH2qr96KET9JeJY3lKF/JCqHyG/qImY72LxCLDevfvED6HNgQQstvb9d+zYGVsRiPXLuE0RudsqciXkKqs5tH1zRSHqgoKCgjpH2aMuKCgoqHMUoi4oKCiocxSiLigoKKhzFKIuKCgoqHMUov6C4df6fIvV0fgyuOXM/cqHOtedCQQWjY9z+xdlXFYbHou6ZGWdOxCyUrJekct6JKBkdDtdwSdtSGVJvdlUW+Zx4f51dyK5tdB4PJq2L6hQiPoLhEH68ssvh3z4F7/4eXrxxRcbBm/t38EGtcFPXAEHU5o57tVXX23IN6dJuk2x6P/5/Mb5es1/td/V/t+5SPmll16Mz93jnA2gGh9/oLZonP6BJn1T+deWtaljDnQ8cvFk9tp6HKqMTz/9TAhbDuf4pvqyqTIdrJ4Hq9fhfnckeTX1d6B6Zo+RSki0+VPp5PP4l1BNTp/+StgAEA5lhevhtntT5WquKKZMXyBEGYjtz//8n8Wg+7u/+7uQQC9b9kE68cQT0+zZs0Mdh2CJH6gea2XkGQY5+beBT+V23XXXh2jj0/ltj6eAv/baa/H+0ksvTdOnv5omT54cyrV3330n8rniiivTPffcE4ZB7dt3iElFGEKY0aJFy3TllVeG/wSp/PXXXx9pLlq0ONR26iRK54FBGMJ3g4HQqlUfhqLvlltuaRCP1JafwpC/xqRJk9PUqc8E0U+efEGT5kHTpk0Lebq8rrvuulDYEcN4iO4rr7wS6V900UXhp3H88f2jLTkOEpdcfvkVkRcVH4EKDw7eIn/8x3+SZs2aFXUis/aU9Ma2oeT5vEAsbGTZzz47LfqKYdK55577qXIyb+ID0rNnryB2BlKESMrBcIk/if/rL6rJxlB+C4noXb8Qr1x22WXh+Ldu3fpoe/XSVup/8cUXN7jqXXzxJVFWJOop9DNnVn1+8803hyK0cfs/8MADoWikIiVl1y7UmKT5+v3NN9+M++KvuuqqT40t47SSxO+ItuVNc/nll6cVK1ZGm5166qkN8nzqWOUzLn7zm19HW1C28okh7b/kkks/9QBmbcDTZMSIEUHYo0aNiralBFbG5ohjFlHXrpS1kdrXGciDIU428DkUTMCs6svS7fXrN0Q6tikQHOMhhMmljhPegcDZjqPbW2/NjonQGFRtDIK403FrGz16dORz0kmjg2C55ZnERBsUkibqVVddHW5vkyZNCrnzeeedH59Li0eFMnLkM4kRsPxNbKRvAiFRRCJf55pkzm8Mn7t07t//+AZPknbt2seC0hS0Cx8PnhUWiY8+WhtlZcvaoUP7kOCbyBYYk/mZZ6bGAuM47ah/KDaRCBKXlrFJTakdXeU0BT4uN9zwjXTCCcOC1Jgu9e59XJo+fXqTW0cu821LIWtETe590UUXR1siV+2lvZXpQPW84IIL4vzJkycF0ctnyJChIePXT2T0FnCvymG7iVz9jTfeiPyuvPKqkMs7R1/yB2kM7WE755prpLMs1IvSseBqy0ceeSRk7RbyxnaqgECZilmwqGpbtWodEnjteNNNN8VYQ+T6GeSh7yyG/pAwSwRlbsrTRJ7am5jIONm6dUu0Z7anbY44ZhE1EtIhXk0CkWC2Wvy6giLstNNOC+nu4UBkg/RMqNxOzuUtgUR9h8CR3pgxJ0dkdiCIvvhiVLLmA+cp6kP8ohuTAoGJkCpl26Z9EuZ2UY4skxZR5T/9iFhEUZU0neHRrpjceSJWdqcb4jPEyWzJse3atW04phYiKKQuCldXPhQWj27duh5Uds8DpVev4/ZF/u0bjKNOP/30iFCRLjc6Hhf9+/cLQu7Ro3ssAK4U5FtJ0at9dT4riEPeTbddJo3N0W6iT7akDK6auoIR2VeOh8z9d+/z/ajk6IsWLQyHQH4c2QiqMfZve7Lv1nHlxPbW/52nzsaRY/0h1LPPPit169Y9xpV2R+DKoY2ayks76HfjLsvbXVlQW4qAK5OrQbGoZTuC2vHkfF4vFqUXXngh+iBvV1Qy+U8rJJ3Dl8S4V/4hQwZHpCyvxrDQuNJylYes16xZk1auXBELQHPFMSNqg8blWPZ28OqzrzMMuMaXlQeDNrn55m/H/l329hAlGJgffrgqnOT69OkT0a5L3eyJXAuT3qW6CWTic2lranvEBDj99LExkXlbmEj2lJ2HlF3S2lIw+EVGojiw8LjsHjv29Ehj3LixEZ198MHyyAdRde3aLSJnl8xIjm2r7QueEy7xLdjahalSU4uYCa3stjlMSPuYtiG45DUlgzbxs0f3KaecEgsDyOv5558Lx7jOnbtEtM0ZT77PPfds7EXbohg/fkKDkZM8XRVoR/8X1blUbwq2hJDeyJEjg4yuuOLyKCdXwOxvXQuf25c1/nlwDBiwKYj1tNNODec/fawNRf1NwYJT617IQ0XfWMSVF2mBuqgTv26R+ptvvpXOOeecNGHC+PhOn77wwvNRbtsXjSGade7Mma8HgYr0GXpZCLXvmWeeGYub4/R9YyijbSf9oR2YU40adWIaPnxEWNIaQ8qnzXCC1x49eqYxY06KKzBbPyxZXVHJuzGQPdfC888/LxaMZ599Ngyp5Ndcccwk5Aan1d/AQTw65+tO1EeD2uYW1Wi3A0XkTZHWgbqr8bFH061NPZ7qWOBg9WicZ1P1sKCY/PmBAJ9HWY+krY/m+GN9/pHgQHmJpp98ckpslxzIJ+TzLuexaPfmgGNG1DpdBIN8RGJW/6ZW84KCI8HBSLzgs+FIn5NZ8OXhmIW8LqEQdONfcAsKPgsKgXx+KATdDIlap9tHQ9Si66/7HR8FBQUFXxRaHkuitoeIpIktGt87W1BQUFDwJUfUfsl3f63tD7/ONnVbVkFBQUHBl7z14Vad/GijElF/PX+Uq71bo6Cg4IvBMf3lz436bjXLngoFXy6o2ogg9IfbAHkuHO5vB8QxrpBqj/deGhRo1ISNv6Os85DVQ8FiThV4oLIwjWpKUfd541AeFF8EzJv8BPSjAQWfrccDwT3u/j4Lqv5744DtRC7elCL1QJCOceo+/y+7/ZsFUdf+ilwa+9PQJm5fpOgyKJEh6S4S9edGfzJoE42yzPHeU41VTxXfHa/+b7KQGEsvP5DWJLflJC3fVa8fNpAoVaH+qU2zcT9VisVKAs1zxP9NOp85j++Ee+TVoXG+leR5TxyXn0ie6+I4aVH5Oe6ll15ueHq5OteWw22euW65fbJrYK3roFfnK5u0pKNeOR/ldl9/NhKqniheqRzz+9ryqcfdd98VeaibvCvPij3xnWNz3hak2rrn8uX85JH7EPHm985RLucoa34vbWVynPfqLH9tkPsgt2FtP+XzM5yvDOqffUNyGeS3ZMn7QYq+z+3k+Pxk9ex6l/vPd86tnj5fHa9cFutcz9wGGcQz/D+UL6eTx32t9F5+PvOd/la+22+/raFM8snlWrVvPDdXHLOtD42pgYlcKNuyXLop1VxzhTb6zW9+E7Jm8tkXX3wpHkpKykuuyzCJeRDlH6UgFSPfBWpCxEl2/etf/yrMizxQlvTb4KZoY5rkioZCjfCofft2aezYcaHCsxX1xhuz4onaPDVMMv1jkvzpn/7pfupKk5K7H3k1vwXKPGoyn0tbORgLZXXfE088HvJm382Z827cP1+ZJnWL3yqQbvfu3SKv3/u976dbb/1NSMZ5TZClMw/SLmeddXYoBPNCz/BHXaWBfEaOHLXP82NiGjFiZBxTmSa9kgYMOD4k1PIywXmVcAmk7KRavOGGG4KAqQOp9Sg/jVMKT2IaD+YlUabIc8Wg3aTbuXOntHLlh/E5Jd2ECRNDuQcLFixM999/X8jWHa9+iARxXnLJJenxxx+L/Mj8qfwQGan3tGnPptGjTwzVqDx4YjBUeuyxR0MJSMHHmIsfhnZDgvqb0pLaT5kBcZHg608yeJ/nJ9sj0HvvvTfydhcWxSdPGu1EBm9O3nnnHel3fud301133Rlta9z5jr/M++8vCUUh0yR+IfxJfP6d73w33X333VHn9evXxVWVp7ErA1Xm+eef39B/5r525tWhfPPmzY++PPvss+NVGY0T/ZyVkdSYuf31ubyN3S5dOqePP14X/dCUkrE54Jje9WG1FBVYCQ2kgv0hwkBK3/jGN4NYDExmQSIlhMKbwoSjwuP1UV3+701XX311yI6RicmHFGfNerPBalQURHbM0IdfAwe5VauqqJ2jHYkxF7YLL7wopNs5TQtGjqxypEauTK7Lqc3E5dAnejThbWsx5EGUjHKkn/OtzHO2Rv5Ll1aX1oie/Pvaa68LMmckhKTV2WS3tcI9DVExAKpF5TrXKkyReHNYrDZu3BATO8NiwTWP1Fu9uPxNmnR+LBiCBeciDJfiyqxOK1Ysj0VH2trcqwVQucjQLZiIUl8534JnbIv61C9D3ZG2sotQX3ttRpAdZzgLCCKWLtMpbYxw9IWFec2ajxq2o9TNXGnduk3kxx8F2Uvf/12haHueHWxFM9TJXPNdrXESxzznHndcr2hnhKdtzjrrzDBhqrw1qiu1KkLeEn2qzPpDn+tDfEtSzp+DdN4CLM+croWYZN8iYCyR+NfCcOIJg+SXL1+xb0x+2BB55205i6hxbzy5UrNo6APHqYfFuW/ffjEemvNtv8csos4mTLUoPzjtD5GrycaHGumJUkS3ItPKaKdV2rWrVbxWpj8tgsQZ35g8p5xycgxcbT1oUGX5OGrUyJgQ27Zt3Sc2ahGKUH/8EnxmAvDTQAz8GESylUFR6yAg6Z933nlRRrdYipQYAjme14R+HDOmf5BMFjQhOJ9LI5v0eBURmswjRgyP9xaM6jiRUdeYrKxeEQR/COVCRixIM6pFoWtEbaw7netqwfEiLl4eUFnCTo8oXzSqLZEWh0CRaM6X4x2HP22MjC2SojyLH+tS9Ud66ofY/CEJeSM0fcXPRPTP3yRv8eW6i85FeggF0chTHZGR6FGZ+FyIsEE5LTIWXv2k7aQhLeZPnTq1D3IXcSMsi4WrjRkzXk1nn31OHKvuFju+Hkg0z7UqjSq9XH/Hvv76G5Em4hQg4Dv1tkWRPXqMNaStrbwywOIgqK/V1XEzZqyMulm0eHXYqzYma+e++tgScYVjnCqHtNigWrj0lXJaTM0FbZQNsyzurgpsu/GbMUfatWsb7ctnZfjw4ak54phJyMHE1jkiPB3HYKjgE2hql8gmqAjPBHZ557IxOqMF17U9MWARhclmqwA5aEuucKJBA925HNmQi2gQKZhseY/RlohB7XjH2A6xIIjIkZjPkJNtlzlz5qZrrrmmwT/DVof8TUzE7VxlUU7nKot6iHJFkUyfWKAiAemKEP0heeXhbFfl2TMmq/JxyjN5RWk57exIhyiRCeIQ4Suz+iEE7xGnsoqwlA2JIyNpiRjli4C0l3LI396saFybSAchMJj6+c9/HosUfxp/7FK58yEmEZ87mdq25SC4KEjH4pP3+S1yyB8BynfBgvci0nQOQgJl0Z6OVUd5q6/54UdTfWaLwcKlXV01yFu7+dHP+Y6Rdr9+/aMueZwoH0JUJ20AonFlsXVmSyfXP7dzJsm8/297BeHaYrOgMbXS5vpX3trYYiIf/S09JO0z6aqnsYLY8zan6Ne2nLFtPBkT2brUQua4vF/vfO2FO6SnfYzTag9+Q4ztVq1axZWQIAO5N8cA8JgSdf5xRafoyKbc3woOH7rGZatJ8Hnd7qjPTJyvsx3twdrX5Tunu+ZsIFaNs3fCd/tI3CALvoJELeIRWYjgTP4c5ZT7qQsKCgrq6MdEkZlLQpdC+X1BQUFBQZ0QtX2vvE/qRy5kXWxOCwoKCursmYn2Of1I4UeE6sGozW/Tv6CgoKBuiVo0bX/aL7T5uWoFBQUFBXW29eG2qnzvZsGBJeS1MuAsfMhCgPz/I32Ku/OzvNrtU0eTxpEgp91cBQgFBV9Jona/JXIQVbv/1v2OBZ++6rjrrrsa/A7c6+zGfvewVk+vpubbnO6777647zXfi3s4cE8umbMfcH/6039Kb731VggajjWUOf+Rlx8ust9Isb8tKPiSlYkURn5EzPLPcmvep8nKjfuPP/54qAXdH02cgPSee+65EHKQZBMXaDtXJ++9Nz/kvZR7nhxNyUWtddJJY0KwInqmTqMQo1AjA/YbAWGDe9od46nnRAV+N3C/LLENcQeFHLkuIYZFlgz6o4/Wxmd+EHae3x1IfPlViNg/+GBZ2rhxU8iI3XPrPm/5uaLi9aCs5OREOvw8PL3ceKAW/G//7W/Tt799S6jMSNOVQV4vvvhCiDlqvT4KCgo+p60P902bfEiiOQooDgcIEeFOmzY1pM8EF9R9vBAQHAUaUKV5/8gjj6YzzjgzjhclU5MhZ1soTz31VJg7IXWEx7jH1cygQYNjASC5Fa1T85F16xMqtEcffbQh+iZDBspCBkX8Kx5++OEwBXI/vPIh46effmqfXHlgKOEqT4vZIbOmWJs/f14sAp5qLW+GOzNmvBYL+NKlS8I3wvhQlnvuuSd16NA+PfPM07GwiLSp0goKCprGMf3FD4EgkxzVFXwaTG8QFtJE0FB5N/QPk6Ra0yEeHnwTkJjoFcnbErEoUn36nF+FSBgZMtDhiyByzlczvEX69u2TevbslaZOfWafj8XONG7c+CDYKVOmNOTHX8NCK6rls4FkJ0yYENE/HwsRu3P8kTkD+fgJJwwNqbkrAdGz2zInTZoc9fKdvF1NqIOyUa4yIWKodOqpp4Qy8MEHH4zx40qsoKDgcyRqBGIyi6jKJeynoU2Q2B133B7kx+uCHwVPBj7P/BWqbYf+cZzthyFDhqbf/ObXQeRITiQqmhblamtwrC0OnhEdOnRM/fr1DZKUB/tQEaz9aj4UHPmky3jJ1ghizmD8dNttvw33vosvvjh8HZYsqbwpnAPytL1hL13Z5YWs1cH2jG0N/htMoiwQLDBdYSF3Xha2Wy688MLYf2chyrSJZ4VyuALwvqCg4HOSkNcmIzISuWUiKfikjfyJOrPLWe0dIOBz72sXumzQ5GqFe5xo2TaE/ex8nB8oa9Osfc3fQW3eOV3vRbkPPfRQuummmxp8L/KPnu7iqS2Tz/NnMYga5el7aTQuR65/Fkfl2ziVI98pVPvwiYKCgs/B64NLWH4ahx8WcxRWcGyQHccQ2bEWFOUnw9iSKURZUNAMIuqGxEtkVFBQUFB/T3jhR52fsVdQUFBQUGc/JrrP1uVzFkQUFBQUFNSZ10d+OonbtOxRFxQUFBTUGVG7vcwdH27FKhF1QUFBQZ0RtVvG8j3Uflhszo82OhC0i+0hd254tbB5zU8Cb+p4gpWj9cfIj0Y71O/F2YfDU8IP57fl/OxH93MfLSgbD1RvvjEUmEf7O/fRGEY51m2C+UnxTZ3rO142h5tefizdoaB/3Xvut52DQXrGy6E+a6osWdH6RZhrqUd5aEgd71G7tYukuXoi8ydCioIKJsM//dNP0v333xdPeibZpuD0dGmTNXukeM33ML/22syYiPm7PKmQWS2B5/uza4+hEPWbAUgvb0/lY3Je0mcWlT+Xdk4jH1Obj3RIypFEbXq53LV/+fjaY7z3IFyKyto88rHuwX/iiccjP9/ldGrL0FRb1JaBJ0p2KqxNo7ZctUBiv/jFL9K9996TnnjiiUincTsQGWnT2u8O1P7ev/76zIYH0ea2rz0mp0FY9Pjjj32qfxrX1xPT2Q7Unvv888/Fw2hznapjPym3zx375puzGu6dr62/74ihcvvVtmvj8VTbT7lOtcfmMnl6OqVsbbvkuuXXxn1RO/YPlPfeZuzUeMzCXpOCV0VuTCo0JkMFnyBbmJ577nnRPvw8fCZKo/abPv2VtG3b9ngaNKXhd77z3XgS84cfrgzy0LSemE167nhKw2uvvTYmoAlMhg3jx48PIQmvEOnwFnn00UdCNu5ed+dTHyIeYPC0cOGCMFu6664744nYPDkoG3l5jB8/IZ188slxLLc/k95TuakeOf2JjBkqIW518n+/UVgAbrzxxvT000/HU8GvuOLK9MYbr4c0/oILJqf16zeEQvHVV6dH3a677tp01113N0Tq7iBi2GTSfvOb32yQoiMnboGnnXZ6PPF66dJlqVOnjvEE7QceeCDu33/22WfjCese2PrSSy9GGldddXX4iyjbpZde1vDUbGBqJbjwNHakoC/mz38vJPojR44MzxRtec45Z0fa2nH06BOjDbQ/MZIFlwXAlVdemRYvXhTl6tr17chb+ueee24YXenf448fEP1z3HG9o07mju1CPiwI7+yzz25oc+dKm5p07Nhx6cknnwxy1z/Gk3NatWqZbrrp5jR79uxISx48X/jBnH766TGuRLrMv6hGjS1Xv55kf9ttt8V7aXkKO6XrmDEnpSlTnkx79uxOl112eSye8+bNTZ06dY7xdf/990efX3DBBVF+XjTKZyxV5PrJuL/77rvSli1bYzxw1fSnLahx9+zZG+31yCMPu3csnlR/0UUXx7jasWN7OvXU0+Lp5cb3hRdeFPYLzRHHLKI26DWiSebHxBzJfZ2BlKj5bAMcLrZu3RZGR/kyN09SE4HZ0ahRIxuk47YjREsMjXiEXHXVVTERSc+1NXLIkbeIkAjmW9/6VtiP9urVKwyYmC9ZCBDDmWeeGWW9/vobYptKPi65pXXyyafs27ZK6bvf/W4QKve73bv3RFq5rIj65pu/Hc5/yqY8Xbt2CTJBHhz0pINo8vZGpVBtEXkhO74gvD5I1plIKVvlEvhO6tmzRyw+xhNCvPrqa/a5A86KMiAxeSEarn8Wm9/93d+NMUd+7vwbbrghiOb88yel559/PtJAIBwAkSHpe+MHW6grqTvStW3nGIvprFlvRHtOnjw5nXPOOfG99kL2uUyQ+w9xyYfknxGW8y1gZ5wxMcqLJK+77voG/xSLHtsAi6hxMGLE8HT99dfvZ1ErQr3xxpvCE0V7Mt/ShvoW0V100YWxGMvXQuS72bPfjnyHDTshiDo/dUlfLFv2QcMWDjK3IDDjsgAw6ZozZ06cq/4XXHBhjLMpU55I3bp1j34yhrTzpEmTIo2qTfpHm6hvLfIVw7XXXhNltxAqi6tJV1XIm3+NfrGov//+khhT+kKdX375pRgzxr/fvporjvkzEw1yk8z+6NcdTJFEBtmB7nDAmEiUgKBqQRsk0mjbtt2+1zb7bTlUn7WNzywOrlZEUbWXg+xPRR7t27cL9zp5ZGm24/WR7SlE+uSTT6UWLVo2mDfJn6ETwjOxfD5s2PB0yiknh5tehjR8j9gco+6i8yuvvCLKw4tE+uqJjJHPzJmvxXHKeskll0Y0irRBxAfS8odA7KdWNrltIhJfs+ajhvaSvzZQBuQoOs/75epWtQt5e4q6GIuORTyitauvvjq2EFylKEduP3VFdMqL/EXmyAgJaRd5KIvIVPSHhPfXc1WEpOxt2rSOKxgLai5z585dIi2Eo3wPPfRgRK55wfCdtlNXadSSkuOr+q6N966kbDF+85vfivM//HBVLPzayJ9F1IKt/Rht5TxcWZiX8qm1LNi5c0fsKWs7iwnCbN++Q8OYUzffDR06NH3rWzemzp07NbSzciPb2vo2hnHA88WY0EfGibR5wVxyySWxEPtcXr7zXl3VmXfN2WefE31+JP7nXzccM2UiIGo/nuTVuDEZNXfky/Z169ZHRNWlS+eIqhCGqLV161bhgIdcEI+BjXgdm71TRI0GrVfkd9ZZZ8Vks4Vg71tUZ2AjFYZKHPlc/ovkBg8eEsZHoieENHv2WzHhJkyYGFGNz00G53LNQ4KiNhGXNECUb39d1MZrZO7cuVFG5yqzMjrG7xTqJWqXproNHz6iYWtEdOoyWx1EbMrB95ppk3YyftQbuSAmC6J28Z1tGueIFG01zJnzbnhsy0v0pQ3eeeedeI8ERGXSGDduXKRvIXGu7ROf5X1ke8qLF7+fTjxxVKRbkXuvuMqZNu3ZKOPw4cMiIt20aWNE3RYp54sQRZvKzDlQ+Vyy61/E1r17jyBfi4BzfO+KRDsqozZEhLYmXPLbHssPh+Zh/uqrM6IvTz99bESktoUcry62HEA07708+I/bylEvbekzBlquiGxfuLoSYfvefn737t1iMbEH379/v7ii80O2/rF4yEf72KqxSBiPFgSQhnbdv77dG7aqpCni1yfGkm077WgbyPe2j4wVWynaxbH6bMOGjXFltnTp0miDc845t9ne9ntMidqqbPIZ0Aa4gVBwYByrpkcUq1Z9GJeNDPs/SzoHKlO2A/iyftCptSM4VBkOVo/Pku7BzkeE1VVItaAdTfkaf36kbX4k9T6a9A90/oHS+DzGTItmaktxTInaPqlVUyRQIuovFrVOdgVfPEr7F3yeOKY3O9ufFlUc6P7Ygs8PhaC/XJT2L/jKELUfFexL25/yK27eYysoKCgoqIO7PkTQfq31g5IfNPItXQUFBQUFdULUfvnNpvZuvXG/a0FBQUFBHRG1eyrtT7uljCCg+FEXFBQU1KHXh3stRdXuyS33UKcDmCxt+Vx/aNX+7rwpP+YWFHx9cEx/TLTd4Y4PqjKEcSSKveYAty/+wz/8fXhOUDUSZJDKait/BASEBFRwRAUEHtqSMMNdd9Rz1Fr8P8iulyx5P0QKgwcPDsGE9Ig+bD/5rcCCSVRD/OCJ31R/8vP7wfvvLw5Bi/t+KeFcAfG0yErGgoKC+sEx9yJFFIimsZdCQbWPjxCR6DPPPBPvqdRIl5EnteDcuXPSxIlnxKLndscXXng+ffTR2rijZtWq1Wnq1GdCpUdaS7n1+ONPhGERWTO/CJE0xaG/Hj16puefn58uvviSdMcdd4Qa8Mknp4R/A5VZq1atQ33HFIlvQ+3TwAsKCuoHx5RN7VFnz4XmbKByIPiR1f69yBkhMvphXoWwRc/ImgPczJkzw6cC3EFDJj5x4oQwMSKhZTbkHLJbryTF5MKIWBpk6CJo/x816sSImKvzzg/yJ8G2BaOP+FkgeBJlEX+2mSwoKPiaErWJLirj9+BHxYL9URnvdAwLTYY5SJOdaMeOzGraxn3ntjV8nk2tRo8+KbyKn332uTBzQvaVZHxVuNMx2EH+H3ywPN17773xufSky1iINaWFwWc5f2laKJzLJKjyu+YDvTbMdQpRFxR8jSXk2bIQSRc/6gM/4cWfH15F0QgTKYtwRb1eLXbeZ7Mg+9aOQ+S+R9aVwf7aBpczi6R0/S5g28TVDUc13/Fcyefl12xFyxTIe1tVfgD2veOL0q6g4GtK1H5IZF+JpBEGQigoKCgoqLPb85BzfpROQUFBQUEd7lG7/HZZ7laxgoKCgoI6fLht/qGrPC+xoKCgoA5NmRC0H6WIKpp6NH1BQUFBwZccUbsvN6vpisVpQUFBQZ0RdX56s8gaSReF26ehbWr/iIPs5x9IZJJ/lD2cG3MsjiTiX/aPuPLnRe42v/yE9C8DTbVdbvdjkbYrxkP1WdXH7lEvP6wX1NHWB3J2i577d5FQwafb6M4770i/+tWv0gsvvJCeeOKJeNgrWXjt5K4llOeeezYeTNv4u9pjvE6dOjVNmTIl7qE+0DGH+jvc4w7250Gm9957T3rwwQfi4a2fZ15N/fkxe+nSJbFwaROioPwdzxS3jx5NurV9aAHSj/JofAxJvqeuZzz88CPxw/qR5FFQ8Ll5fSAaXhVu0Ssy5KZhMq5YsTJ973vfix9b77///mgrbeZJy3PmzImFzn3oFrsrrrgitWvXPq1fvyGUiSJmMnDHeDq4p1CTf4vunn322XiytKdTeyL5iSeeGK9I5eKLL04zZrwaJE5+7knU0ucZQn7O3Gny5AviSoi50yuvvBwqSATjad2eDM346Y033oho2RPKmT15irT75ceOHRcLjyeNz579dnyGd5STHN7TpT39nEKSdwmPEU/MfvFF57QJnxHHIlJPqZaPJ5L7zFO5KSipXaXNRGro0CFp+vRX07p1H8dx1Jaegp2fUE1d+Ud/9EepfXsPstgRhO23E08nd9WnnWfPnh1mVcrEa0XbKpOtO2X0pG7SfQtlly5dQ47vqvG112bEk8otQohavZ07adL54a1SPY6uXTzkWboWBu2sTosXL4r29L0+YMzl/9OmTQ2PHH1ZhEYFn2tEbSKYRB717q857FEjMgR0JN7bGzasDwMlPh2AqKXBzc52QUp7gwyRKqJlrsTkiuJz9OjR6fnnn4/fAgYPHpKeeuqpIGJEP2TI4CC5t99GlN1S//79g1yR8Pz584IwhwwZEgvCjBkzQsL+5JNPBsH17398pAFvvz07yALpIRrleP311yNSfPfdd+Ip54jQIuMunxkzXou6iKQnTJiYTjhhaDrllFOC8FxVOVca0kJiQ4YMjXQWLVK32VEm+VFSIj3EZsGHakF6K5188ikhj/cj9eOPPx7mVMhWfo8++mi67777Qg07ffr0IOfhw0ekPn36RruqN+fBcePGhucJnxNXHhdddFGQrvy0PRJ/8cUX0yOPPJJatGgZ7S5v3w8ePCjKqDwWp7PPPjvKZ1F0DEydOm1f/24IY63HHnssiF4b+eyxxx5NPXv2ighbXUn8LUL62OKNqAsKPneiJkdGJiafSYRsmgeO7JK1W7fu6Rvf+GYsarVABAMGHJ969TouHX98/9SjRyUFz+DnIQLbtWtnEI3FYffu6rLeuQiBn0ebNhUZLlu2NEiy+u2gTbr++hvSI488GpGgYwYOHBSR7A033BAkq8/grLPODln6lClPNGwlbNmyOb7jC0Jmnvdo/f/SSy8JGXpV5h5xBZB9RfypgwVo584dsQjMmfOu2sa5V155Vbr//vtCQq/8AwcOTA899GAaN25cQ2SJwOTJkrVz507p2muviUXAe5/v2aOMW1OnTv//9s6tN66riuNr7hffYo8d201c0tI0bShpCFBUAm2K1IsAVYgHHnig8Iz4EnwFvgES9IWkJSrQlKoXAYEkbdNWTdI0bRrbqeP4El/mfke/Ndn22Bonk2bsnhmvnzSamXP22bc557/XWWfPXnF5+umnVaDZz6AAtX3dahX7/b6bfvOq3tFQJgPlyMjoSt9ms7W/2LPC4P79D8uRI0/JsWPHtB/c6oIYIYgt25zoOvHWM6Jae2ZAOurFZ6B/nnvuObXYZ2auq1uGwfTgwYNy7NhL2g9cR4axaa4PTlhOdm6pOSEREt47GaxOd7vdDIgPq9vhw0VMsXpZVwMXBtsR1K6uggol7gEudCzn3t4eTYvVu2vXbr2Yp6evqQvALSeL8CKSWMcITW32TVE/Yz1z641FihsC9waW96FDh9SaRHyGhgY1Hyw8plniOqGs48f/Kr29fSp8pH355ZdU4Cnr1KlTOqAgdJQPw8M7VRSpL+fD4GBCXnvthO6nfAYEzgsEkgGC+rr59wgWy6/i5gAn3pxbuCWoM/tYlxvXAiLPCoTUH1cCv8UTT/xQBwes/D177lO3DBb39evTusY3baFvX3zxz5o3Fj19hUXOGt+4dbB0cWeQH2XiIiEN9aB8ymUA4c4RC/38+XM6wNUWvYppvhxz9OhR/c0YZA8ceFStdFYzpM0YNaTjesFq5xxgEGOgXj+IG0ZL1/rAWuEExILiVtKivKyl/iHa3foisca4Rb91mtVyXNnuO0KJyNdv5+VmLbgBwOWBeOPnxgpe3cdshtpxzdZjfd71ZV+4cEEHkSNHjqzUxe2vrxdp3n//rDzzzLMrebGvvg31dVrf9vX1aFRfl74+nct7ff4uTX2dG5VR/32jz+vraRgtF2oEGouJWz0sDiwGozNATBiIsSw3C84bROp2Uzu3oi6G0bHT87AKEGhu/2wedWfBb7vZwoh7pZnzZivqYhgd6aPmCT9+QaxqLjb3MMcwDMO4O1qmpjz4wZpmGhcPwZjKZBiGYXgwZiJ+RubBdvqMD8MwjLYTah7wuHml+BC/ynUeDMMwOomWCbXzS+Or5sn9nfxbzzAMw9iYlj7xq61zENZ/W9mKYYZhGB4UasSZh4m8s7CQYRiG4TGhxprmL8vMozaL2jCMraBarcqVZEHm87U1VW5HTyggD/bVli3YlkLNw0QEmgV7LMqLYRhbxe/fnZYXP20uoPaTo93yyo8fkFD76HRrhZq51Mz2QKjdwjqGYRibSVVEipWq5MvNrYZB2m09j5qZH8PDwyrUNuvDMAzDg2t9sLzkwsKCivV2CBxgGIbRVq4PXB4sfM5iOfx9HKuah4uGsZk0WsL0bvJpRV6tLKeZ9q1fDrbZvDeqz5fp0/VxJd2Ss7cr704X7/S10QNAz/7hhUXwWdSez9u1Q42tBeOAKaGNgsWyQBjnY7OBZQk03CjIcKtewEN21t2GZgL98oCekF+3yhejiPXC+cx68I0CHN+qTufPn1dDy227ePFi0/3mXkR0ItiCW5O+UTm8s5/y3HcgOAP9ThAFAkAQto3fgvfx8XGNpUlYNULfbVdaZlG7RdY5EQluaqvnGVsl1FzACBWCRVgsYkxyZ0fgXt6JNMMzEwJZEHmGwAPEnUQE+HMW35lS6vJDGPjOcRMTE3pnSNQVIrGwnTBeDA5EweE8R3gpmygtxEdEtAiaS3qi2nCHybWxf/9+fUfQCIlGuXv37tV6URe28/8DthMVhn/50gYEjf0IGWVSJ5ZpwL3IftKSL9tpO9upHwMV/UGkGla2ZNE00hPVBhcledJGyqXOxNOkvpRDO9hHW0lHHrSVfF0d6TvKIC+OYzIBIk9f86JMBg0i4TBtl7IRYPaRB5+pO2XTRnSD/uW3QUfIDzfqmTNntN3bORBJS9f64CTnB3FWtWFsBVzkBK5lphFihdAgZIgL2xBxRAoR49xEILBSEUksViDIBenYTlrEg3TcJSJkCBDviBn5uHiWrmzED0sSg4WBg20INELDtYDYIbz1daZsF8gX4aNcZ90SEg2hQ/Bd9BfEk+2II4MC5RFejP3UiTIILExdyc/1B1AXtrtVLYltihhShrN6qSeDC21gYGIfgxBlkDeDC8LK4Ed60pKGoMuuLQxY9CV1ow9ZnI26ANvocwSfMGy00wV5rv9nMy/qQHkMDKVSSfOojyG63WjprA9+QH4YOpyLxTA2Gy5ohApLjPiEfEfwEDSEB2uP8xEDAguN7W7xMEQX4UZQEFbyIR2igDiQXy3mZHjleIQTaxCjxAVwRjgRKfZjjTvXH5YhwoXg1N9hUg6i5cJ3uWuHMrAaeSeUHfuwQhE0ly/pKJvt5EkbKIPPiKcbXKg7feLKwPplEHBRl7CUSUM5HMtx9IsbFGgPZZGGfS5mJHnTh07YgTogorSfgYABj/zdb1KvD+SLUcd+2smAUO8qdb5t6oGoU9/QzXq4IMHNEAv45Gd7+uSFBwfk/t72f1bWslBcdD4nLicVJy4/vs2lNjYbLl5enH8IBcKLcLmLm9ts3t2FjlXKZ9Ii4txuIy5uUTHEiO2IjLPsnAhxfjuxpAwsdi6fkydPqlWJkUJeXAOINO+uHPJGnOr/GIaFTTrSkJ/7HwLp2Q5spy6unU78KZftWPnOJcA22ku96AfXH6SjrNOnT8vhw4dVBMkLyx/hdeHPOJa0fOcapiw32Lno8/QJedJOjnXPAkhLvrSN/qJfKdNZ37xznCvD9bNrpyuXY+pjebqBKZfLaVrnolqjPdWq/OqNcfnTpVUfNsNNXyQgv3ygX05MJuXT5dW7mafu6ZYTP90rIb9vewo1Hc+JxknJD7VR8FDD6BTcwzwnNF6F6xKhdHcdnUSlgVDDjnBAXtg3IH/4aFbq/+PSjkLd0oeJWBSM1Dbrw9gucN5zC+91uCYbWaOdzOMjXXLqenqNSLcrLRNqBBrfFbc03L6MjY3prZFhtAuN5iPr9pvv/i8xN9nl16iMW+exWnLj+ci33n+7ut3qGCxUt/du5mPfrsyVaXs324OBu9Fc7ioPPO/gN4B3ZjKy0ORCTV6nZb4JfE/cWuH8x+WBG8Qw2okv0kU5fmVJhWo2V5KXryypOLwzm5FrmdW5yYv5siQLq/OO17+uZUrqE2XtiTOzmTX73pvLSrbczPzmWtob+bLM50oN95+dy0qmtDrv27FxviLvzmXWlJ8rV2Q2u3be9ZmZjHx4Iysfztd84U29ROTUTEat1/Vzp/97PS0XFnMr28uVqlxLF2WxUJZzCzl5ZXxJ/jaxtKY8fgtWxHPfx1MFtY7vBH7DUgdY0y21qHH646dmChD+sO0859FoT66mi3L08qJ8d2dc3p5KqcgeGIjJe7MZCfq65NyNnFp9ny3npVCuyjNjvWq1PdQflWSxLLPZkjy/p0+uZQoyky3JUDSkopcpVmQiVZCH+6OSLVXkzExaHhmIyfmFrIT9frm0lJPHh7tVRPGrpksVuScekny5Im9PJXWg+PW+hNbn670RXVRoKl2QRDQoi4WgDi6hgE+e/1qfBP0+efOLpJZ/eKRb/j2d0vQ/v3+HdAcD8u5sRgI+n5y7kZVIwC+D0aAK5e8eGZJTM2npjwSlUKlqPTBe35xKycXFnHx/uEvbwL5vDcbl5HRKy3p2rFdenViW3d1hyZUqcjVdkH9dS2lbDw3GtWz6Mh70y+6usLz0+aJ0h/xyYnJZfvuNIR2I/j6+LL94oF/bTVs/WcrLQzuiEgn45B8Ty9q3D+6Iynx++4b3a5lFzRNZ/kyAvy6RSOhkecNoJ7ipfmw4Lq9OLqu1t6srJBWpSk84IG98kVRBJc19PREVq+NXFiVfqagwfTCXle8MxSWsD6hwnZBjzZzDOv3ecJdalhcX85IuVuS1yWUV/r9cXlDxQ/gQxENDcflsKa8DAt93xkJyMBFToSPv168uq+X57aG4WpyI8r3dYR0APk8WZCFfVuGdy5Xkndm0WuO94YBcrpv1gDVLfqRBNB9NxOTsfFYHqtMzaRVcLPWPF3LyP/XxVmUqU5T/TKfl06W8nJ3LqEgnixV5/WpSxf5AIibnFrJqwbKP7XB5uaDvCPhkqqD1eXK0R/b3R7VfpzNF2dcf1b6mvW9NpeQn9/Zq+VPpoub1HncWuc5wYXzlQo0vifml/BOKaXn2hxej3UAcseTen8uq8ET8Pjk5XROugE/kYCKuYoV4jCcLKqJ94YD8aFeP9IT90h+pzQceiQflo4Ws/PNqUnZ3h1QUByIBFVqO/WYiJm9NJVWUR+MhtZ4R8kQkKF1BvxwcjOnAgD8WIcWVwmCAy4XjSTMQCeqsBd4/XszJfK6sdcEKHYqF1KLdtyOqVjfbnWck6KvVge09Ib9EA361vruDfl1Qn7Z0hfzaXvoDt0iuXJXhWEjb8NjOLtnVFVZx5ngGgUtLeRV16oN7IlWsaDmAqD+2M66D2AfzWdkZC2r+DFa0h2OY80zf8JlycaGki2VZLpR14Ij4a3Or2bddadn0PMNod5y/N1uqqrgtF2tCgtWHIKZLNaFEpK6mijLaFVLBHooGhZmofaGABPy1B5FYoAjW/b0R9WcjaEuFsoofgqiukVhQLWGsTdwCxZsuh8+TeS3fifJkqiiJaO2YHZGA1ok8cAmwfyJVs5BH48EV3+yNXFnGukOaJ/ng7kCUebiGmwQLDUue47GkR2IhrTNC79weCOkfP5nXAYl2EBUFn/ZIPKTllCpViXF8qqgDFWVwPJYwLpTBaEBdG9QZvzTtpy7kP5kuatkIeqlaVdGn/+F6tqQDmO9mW4I+nwxEa4MNVniz0/M2oh2n55lQG4bREAac6UxJsuWKjHWHPStslW0g1C2N8GIYRueAu4G7BuOrx/46aBiG4XFMqA3DMDyOCbVhGIbHMaE2DMPwOCbUhmEYHseE2jAMw+OYUBuGYXgcE2rDMAyPY0JtGIbhcUyoDcMwPI4JtWEYhscxoTYMo+3xSWdjizIZhtH2Iv2bhxLyg9HmYrSy/ne7rW1ty5wahmF4HHN9GIZheBwTasMwDI9jQm0YhuFxTKgNwzA8jgm1YRiGxzGhNgzD8Dgm1IZhGB7HhNowDMPjmFAbhmGIt/k/+c2XrA6eP5QAAAAASUVORK5CYII=",
|
||
"text/plain": [
|
||
"<Figure size 372.15x526.2 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
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"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"ref: \n",
|
||
"Los borradores intermedios deberán entregarse en formato Word. El documento final\n",
|
||
"deberá depositarse en formato PDF.\n",
|
||
"1.4. Estética y estilo de redacción\n",
|
||
"Es fundamental que el TFE presente un aspecto elegante y correcto. Se trata de un\n",
|
||
"trabajo académico y debe reflejar la madurez y el nivel formativo de una persona que\n",
|
||
"ha finalizado un estudio de grado o postgrado. Ten en cuenta las siguientes\n",
|
||
"recomendaciones en todas y cada una de las entregas que realices y, en especial, en\n",
|
||
"el depósito final del documento:\n",
|
||
" Verifica la originalidad del documento, asegurándote de que citas todas las\n",
|
||
"fuentes consultadas y no existen textos de autoría ajena sin referenciar\n",
|
||
"correctamente.\n",
|
||
" Cuida la presentación del trabajo. Comprueba que formatos como tipo y tamaño\n",
|
||
"de letra, número de páginas, encabezados, justificación de párrafos, interlineado,\n",
|
||
"etc., son correctos.\n",
|
||
" Revisa la ortografía y la redacción. Utiliza el corrector de Word para asegurarte de\n",
|
||
"que no has dejado ninguna errata. Una lectura detenida del documento también\n",
|
||
"te ayudará a detectar erratas, omisiones o redundancias. Si es posible, pide a\n",
|
||
"alguien cercano que lo lea y te dé su opinión sobre la redacción. Presta especial\n",
|
||
"atención a los siguientes aspectos:\n",
|
||
"- Que los párrafos sigan un orden o hilo argumental lógico.\n",
|
||
"- Que la información se presente de una manera que facilite su\n",
|
||
"comprensión, definiendo los conceptos necesarios e incluyendo las citas\n",
|
||
"bibliográficas pertinentes.\n",
|
||
"- Elimina párrafos demasiado cortos. Cada párrafo debería tener al menos\n",
|
||
"© Universidad Internacional de La Rioja (UNIR)\n",
|
||
"tres oraciones.\n",
|
||
"- Elimina frases superfluas y repeticiones de ideas.\n",
|
||
"Instrucciones para la redacción y elaboración del TFE\n",
|
||
"7\n",
|
||
"Máster Universitario en Inteligencia Artificial\n",
|
||
"paddle_text: \n",
|
||
"Los borradores intermedios deberán entregarse en formato Word. El documento final\n",
|
||
"deberá depositarse en formato PDf.\n",
|
||
"1.4. Estética y estilo de redacción\n",
|
||
"Es fundamental que el TFE presente un aspecto elegante y correcto. Se trata de un\n",
|
||
"trabajo académico y debe reflejar la madurez y el nivel formativo de una persona que\n",
|
||
"ha finalizado un estudio de grado o postgrado. Ten en cuenta las siguientes\n",
|
||
"recomendaciones en todas y cada una de las entregas que realices y, en especial, en\n",
|
||
"el deposito final del documento:\n",
|
||
"Verifica la originalidad del documento,asegurándote de que citas todas las\n",
|
||
"fuentes consultadas y no existen textos de autoría ajena sin referenciar\n",
|
||
"correctamente.\n",
|
||
"Cuida la presentación del trabajo. Comprueba que formatos como tipo y tamaño\n",
|
||
"de letra, número de páginas, encabezados, justificación de párrafos, interlineado,\n",
|
||
"etc., son correctos.\n",
|
||
"Revisa la ortografía y la redacción. Utiliza el corrector de Word para asegurarte de\n",
|
||
"que no has dejado ninguna errata. Una lectura detenida del documento también\n",
|
||
"te ayudará a detectar erratas, omisiones o redundancias. Si es posible, pide a\n",
|
||
"alguien cercano que lo lea y te dé su opinión sobre la redacción. Presta especial\n",
|
||
"atención a los siguientes aspectos:\n",
|
||
"Que los párrafos sigan un orden o hilo argumental lógico.\n",
|
||
"Que la información se presente de una manera que facilite su\n",
|
||
"comprensión, definiendo los conceptos necesarios e incluyendo las citas\n",
|
||
"bibliograficas pertinentes.\n",
|
||
"Elimina párrafos demasiado cortos. Cada párrafo debería tener al menos\n",
|
||
"© Universidad Internacional de La Rioja (UNiR) tres oraciones.\n",
|
||
"Elimina frases superfluas y repeticiones de ideas.\n",
|
||
"Instrucciones para la redacción y elaboración del TfE 7\n",
|
||
"Máster Universitario en Inteligencia Artificial\n"
|
||
]
|
||
},
|
||
{
|
||
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",
|
||
"text/plain": [
|
||
"<Figure size 372.15x526.2 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"ref: \n",
|
||
"- Escribe siempre al menos un párrafo de introducción en cada capítulo o\n",
|
||
"apartado, explicando de qué vas a tratar en esa sección. Evita que\n",
|
||
"aparezcan dos encabezados de nivel consecutivos sin ningún texto entre\n",
|
||
"medias.\n",
|
||
" Repasa las citas bibliográficas. Comprueba que todas ellas son correctas y siguen\n",
|
||
"la normativa que exige la titulación.\n",
|
||
" Asegúrate de que las figuras y las tablas se ven clara y correctamente, e incluyen\n",
|
||
"número y título, así como su procedencia o fuente.\n",
|
||
" Comprueba que los índices se generan correctamente.\n",
|
||
"1.5. Normativa de citas\n",
|
||
"En esta titulación se cita de acuerdo con la normativa APA.\n",
|
||
"Recuerda que tienes una guía con explicaciones y ejemplos en el apartado Citas y\n",
|
||
"bibliografía del aula virtual: https://bibliografiaycitas.unir.net/\n",
|
||
"© Universidad Internacional de La Rioja (UNIR)\n",
|
||
"Instrucciones para la redacción y elaboración del TFE\n",
|
||
"8\n",
|
||
"Máster Universitario en Inteligencia Artificial\n",
|
||
"paddle_text: \n",
|
||
"Escribe siempre al menos un párrafo de introducción en cada capítulo o\n",
|
||
"apartado,explicando de qué vas a tratar en esa sección. Evita que\n",
|
||
"aparezcan dos encabezados de nivel consecutivos sin ningún texto entre\n",
|
||
"medias.\n",
|
||
"Repasa las citas bibliográficas. Comprueba que todas ellas son correctas y siguen\n",
|
||
"la normativa que exige la titulación.\n",
|
||
"Asegúrate de que las figuras y las tablas se ven clara y correctamente, e incluyen\n",
|
||
"número y título, así como su procedencia o fuente.\n",
|
||
"Comprueba que los índices se generan correctamente.\n",
|
||
"1.5. Normativa adecitas\n",
|
||
"En esta titulacióon se cita de acuerdo con la normativa Apa.\n",
|
||
"Recuerda que tienes una guía con explicaciones y ejemplos en el apartado Citas y\n",
|
||
"bibliografía del aula virtual: https://bibliografiaycitas.unir.net/\n",
|
||
"© Universidad Internacional de La Rioja (UNIR)\n",
|
||
"Instrucciones para la redacción y elaboración del TfE\n",
|
||
"Máster Universitario en lnteligencia Artificial ∞\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 372.15x526.2 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"ref: \n",
|
||
"2. Estructura del documento\n",
|
||
"En esta sección se describe con mayor profundidad la estructura y los contenidos\n",
|
||
"esperados en cada apartado de tu TFE.\n",
|
||
"Léela con detenimiento y compárala con la programación semanal que encontrarás\n",
|
||
"en el aula virtual, pues en cada borrador deberás entregar completados diferentes\n",
|
||
"apartados que se explican a continuación, y que se elaboran de una manera no\n",
|
||
"necesariamente lineal.\n",
|
||
"Como ya se ha mencionado, la memoria debe estar estructurada en capítulos. Por\n",
|
||
"norma general, la estructura de capítulos suele reflejar la línea de discurso del\n",
|
||
"trabajo, empezando por una introducción donde se plantea el problema, seguida de\n",
|
||
"un estudio de la literatura donde se estudia y describe el contexto. Posteriormente\n",
|
||
"se establecen claramente la hipótesis de trabajo y los objetivos concretos de\n",
|
||
"investigación, así como la descripción de la metodología seguida para alcanzar los\n",
|
||
"objetivos. Posteriormente se describe la contribución del trabajo, seguida de una\n",
|
||
"evaluación de la misma. La evaluación da pie a la elaboración de las conclusiones,\n",
|
||
"que deben relacionar los resultados obtenidos con los objetivos planteados\n",
|
||
"inicialmente. Finalmente, se describen las líneas de trabajo futuro necesarias para\n",
|
||
"seguir avanzando hacia la consecución de los objetivos.\n",
|
||
"A continuación, te dejamos algunos consejos generales sobre cómo organizar los\n",
|
||
"capítulos, pero ten en cuenta que cada trabajo es único y esta organización es una\n",
|
||
"guía general adaptable. El director específico de tu TFE podrá aportarte consejos\n",
|
||
"sobre cómo organizar la memoria adaptándote al contexto de tu trabajo concreto.\n",
|
||
"Como recomendación general, la estructura de capítulos de tu memoria debería ser\n",
|
||
"similar a la siguiente propuesta:\n",
|
||
"© Universidad Internacional de La Rioja (UNIR)\n",
|
||
" Organización del trabajo en grupo (solo en trabajos grupales)\n",
|
||
" Capítulo 1 – Introducción\n",
|
||
"Instrucciones para la redacción y elaboración del TFE\n",
|
||
"9\n",
|
||
"Máster Universitario en Inteligencia Artificial\n",
|
||
"paddle_text: \n",
|
||
"2.E Estructura del documento\n",
|
||
"En esta sección se describe con mayor profundidad la estructura y los contenidos\n",
|
||
"esperados en cada apartado de tu Tfe.\n",
|
||
"Léela con detenimiento y compárala con la programación semanal que encontraras\n",
|
||
"en el aula virtual, pues en cada borrador deberás entregar completados diferentes\n",
|
||
"apartados que se explican a continuación,y que se elaboran de una manera no\n",
|
||
"necesariamente lineal.\n",
|
||
"Como ya se ha mencionado, la memoria debe estar estructurada en capítulos. Por\n",
|
||
"norma general, la estructura de capitulos suele reflejar la linea de discurso del\n",
|
||
"trabajo, empezando por una introducción donde se plantea el problema, seguida de\n",
|
||
"un estudio de la literatura donde se estudia y describe el contexto. Posteriormente\n",
|
||
"se establecen claramente la hipótesis de trabajo y los objetivos concretos de\n",
|
||
"investigación, así como la descripción de la metodología seguida para alcanzar los\n",
|
||
"objetivos. Posteriormente se describe la contribución del trabajo, seguida de una\n",
|
||
"evaluación de la misma. La evaluación da pie a la elaboración de las conclusiones,\n",
|
||
"que deben relacionar los resultados obtenidos con los objetivos planteados\n",
|
||
"inicialmente. Finalmente, se describen las líneas de trabajo futuro necesarias para\n",
|
||
"seguir avanzando hacia la consecución de los objetivos.\n",
|
||
"A continuación, te dejamos algunos consejos generales sobre cómo organizar los\n",
|
||
"capítulos, pero ten en cuenta que cada trabajo es único y esta organización es una\n",
|
||
"guía general adaptable. El director especifico de tu TFE podrá aportarte consejos\n",
|
||
"sobre cómo organizar la memoria adaptándote al contexto de tu trabajo concreto.\n",
|
||
"Como recomendación general, la estructura de capítulos de tu memoria debería ser\n",
|
||
"similar a la siguiente propuesta:\n",
|
||
"© Universidad Internacional de La Rioja (UNiR)\n",
|
||
"Organización del trabajo en grupo (solo en trabajos grupales)\n",
|
||
"Capítulo1–Introducción\n",
|
||
"Instrucciones para la redacción y elaboración del TFE\n",
|
||
"Máster Universitario en Inteligencia Artificial 6\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 372.15x526.2 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"ref: \n",
|
||
"Capítulo 2 – Contexto y estado del arte\n",
|
||
" Capítulo 3 – Objetivos y metodología de trabajo\n",
|
||
" Capítulo 4 – capítulo de desarrollo de la contribución, título del capítulo\n",
|
||
"dependiendo de la tipología del trabajo\n",
|
||
" Capítulo 5 – capítulo de desarrollo de la contribución, título del capítulo\n",
|
||
"dependiendo de la tipología del trabajo\n",
|
||
" Capítulo 6 – capítulo de desarrollo de la contribución, título del capítulo\n",
|
||
"dependiendo de la tipología del trabajo\n",
|
||
" Capítulo 7 – Conclusiones y trabajo futuro\n",
|
||
"2.1. Resumen\n",
|
||
"El resumen se redacta en último lugar ya que recoge las contribuciones más\n",
|
||
"importantes del trabajo. Es necesario tener muy clara y completa del documento para\n",
|
||
"poder resumirlo correctamente.\n",
|
||
"Tendrá una extensión de 150 a 300 palabras y deberá ofrecer una visión global de lo\n",
|
||
"que el lector encontrará en el trabajo, destacando sus aspectos fundamentales.\n",
|
||
"Deberás indicar claramente cuál es el objetivo principal del trabajo, la metodología\n",
|
||
"seguida para alcanzarlo, los resultados obtenidos y la principal conclusión alcanzada.\n",
|
||
"A continuación, indicarás de 3 a 5 palabras clave o keywords como descriptores del\n",
|
||
"trabajo que lo enmarcan en unas temáticas determinadas. Serán los utilizados para\n",
|
||
"localizar tu trabajo si llega a ser publicado.\n",
|
||
"© Universidad Internacional de La Rioja (UNIR)\n",
|
||
"Instrucciones para la redacción y elaboración del TFE\n",
|
||
"10\n",
|
||
"Máster Universitario en Inteligencia Artificial\n",
|
||
"paddle_text: \n",
|
||
"Capitulo 2 – Contexto y estado del arte\n",
|
||
"Capítulo 3 – Objetivos y metodología de trabajo\n",
|
||
"Capítulo 4 – capítulo de desarrollo de la contribución, título del capítulo\n",
|
||
"dependiendo de la tipología del trabajo\n",
|
||
"Capítulo 5 – capítulo de desarrollo de la contribución, título del capítulo\n",
|
||
"dependiendo de la tipología del trabajo\n",
|
||
"Capítulo 6 – capítulo de desarrollo de la contribución, título del capítulo\n",
|
||
"dependiendo de la tipología del trabajo\n",
|
||
"Capítulo 7 – Conclusiones y trabajo futuro\n",
|
||
"2.1. Resumen\n",
|
||
"El resumen se redacta en último lugar ya que recoge las contribuciones más\n",
|
||
"importantes del trabajo. Es necesario tener muy clara y completa del documento para\n",
|
||
"poder resumirlo correctamente.\n",
|
||
"Tendrá una extensión de 150 a 300 palabras y deberá ofrecer una visión global de lo\n",
|
||
"que el lector encontrará en el trabajo,destacando sus aspectos fundamentales.\n",
|
||
"Deberás indicar claramente cuál es el objetivo principal del trabajo, la metodología\n",
|
||
"seguida para alcanzarlo, los resultados obtenidos y la principal conclusión alcanzada.\n",
|
||
"A continuación, indicarás de 3 a 5 palabras clave o keywords como descriptores del\n",
|
||
"trabajo que lo enmarcan en unas temáticas determinadas. Serán los utilizados para\n",
|
||
"localizar tu trabajo si llega a ser publicado.\n",
|
||
"© Universidad Internacional de La Rioja (UNIR)\n",
|
||
"Instrucciones para la redacción y elaboración del TFE 10\n",
|
||
"Máster Universitario en lnteligencia Artificial\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"from itertools import islice\n",
|
||
"\n",
|
||
"results = []\n",
|
||
"for img, txt in islice(dataset, 5, 10):\n",
|
||
" image_array = np.array(img)\n",
|
||
" out = paddleocr_model.predict(\n",
|
||
" image_array,\n",
|
||
" use_doc_orientation_classify=False,\n",
|
||
" use_doc_unwarping=False,\n",
|
||
" use_textline_orientation=True\n",
|
||
" )\n",
|
||
" show_page(img, 0.15)\n",
|
||
" print(f\"ref: \\n{txt}\")\n",
|
||
" paddle_text = assemble_from_paddle_result(out)\n",
|
||
" print(f\"paddle_text: \\n{paddle_text}\")\n",
|
||
" results.append({'Model': 'PaddleOCR', 'Prediction': paddle_text, **evaluate_text(txt, paddle_text)})\n",
|
||
" "
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "0db6dc74",
|
||
"metadata": {},
|
||
"source": [
|
||
"## 5 Save and Analyze Results"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 15,
|
||
"id": "da3155e3",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Benchmark results saved as ai_ocr_benchmark_finetune_results_20251207_155752.csv\n",
|
||
" WER CER\n",
|
||
"Model \n",
|
||
"PaddleOCR 0.104067 0.012581\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
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",
|
||
"text/plain": [
|
||
"<Figure size 800x500 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"df_results = pd.DataFrame(results)\n",
|
||
"\n",
|
||
"# Generate a unique filename with timestamp\n",
|
||
"timestamp = datetime.now().strftime(\"%Y%m%d_%H%M%S\")\n",
|
||
"filename = f\"ai_ocr_benchmark_finetune_results_{timestamp}.csv\"\n",
|
||
"filepath = os.path.join(OUTPUT_FOLDER, filename)\n",
|
||
"\n",
|
||
"df_results.to_csv(filepath, index=False)\n",
|
||
"print(f\"Benchmark results saved as {filename}\")\n",
|
||
"\n",
|
||
"# Summary by model\n",
|
||
"summary = df_results.groupby('Model')[['WER', 'CER']].mean()\n",
|
||
"print(summary)\n",
|
||
"\n",
|
||
"# Plot\n",
|
||
"summary.plot(kind='bar', figsize=(8,5), title='AI OCR Benchmark (WER & CER)')\n",
|
||
"plt.ylabel('Error Rate')\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "3e0f00c0",
|
||
"metadata": {},
|
||
"source": [
|
||
"### How to read this chart:\n",
|
||
"- CER (Character Error Rate) focus on raw transcription quality\n",
|
||
"- WER (Word Error Rate) penalizes incorrect tokenization or missing spaces\n",
|
||
"- CER and WER are error metrics, which means:\n",
|
||
" - Higher values = worse performance\n",
|
||
" - Lower values = better accuracy"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "830b0e25",
|
||
"metadata": {},
|
||
"source": [
|
||
"# Busqueda de hyperparametros\n",
|
||
"https://docs.ray.io/en/latest/tune/index.html"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 16,
|
||
"id": "3a4bd700",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Python 3.11.9\n",
|
||
"pip 25.3 from c:\\Users\\Sergio\\Desktop\\MastersThesis\\.venv\\Lib\\site-packages\\pip (python 3.11)\n",
|
||
"\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"!python --version\n",
|
||
"!pip --version"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 17,
|
||
"id": "b0cf4bcf",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Requirement already satisfied: ray[tune] in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (2.52.1)\n",
|
||
"Requirement already satisfied: click!=8.3.*,>=7.0 in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from ray[tune]) (8.2.1)\n",
|
||
"Requirement already satisfied: filelock in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from ray[tune]) (3.20.0)\n",
|
||
"Requirement already satisfied: jsonschema in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from ray[tune]) (4.25.1)\n",
|
||
"Requirement already satisfied: msgpack<2.0.0,>=1.0.0 in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from ray[tune]) (1.1.2)\n",
|
||
"Requirement already satisfied: packaging in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from ray[tune]) (25.0)\n",
|
||
"Requirement already satisfied: protobuf>=3.20.3 in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from ray[tune]) (6.33.2)\n",
|
||
"Requirement already satisfied: pyyaml in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from ray[tune]) (6.0.2)\n",
|
||
"Requirement already satisfied: requests in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from ray[tune]) (2.32.5)\n",
|
||
"Requirement already satisfied: pandas in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from ray[tune]) (2.3.3)\n",
|
||
"Requirement already satisfied: pydantic!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.*,!=2.4.*,<3 in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from ray[tune]) (2.12.5)\n",
|
||
"Requirement already satisfied: tensorboardX>=1.9 in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from ray[tune]) (2.6.4)\n",
|
||
"Requirement already satisfied: pyarrow>=9.0.0 in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from ray[tune]) (22.0.0)\n",
|
||
"Requirement already satisfied: fsspec in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from ray[tune]) (2025.12.0)\n",
|
||
"Requirement already satisfied: annotated-types>=0.6.0 in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from pydantic!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.*,!=2.4.*,<3->ray[tune]) (0.7.0)\n",
|
||
"Requirement already satisfied: pydantic-core==2.41.5 in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from pydantic!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.*,!=2.4.*,<3->ray[tune]) (2.41.5)\n",
|
||
"Requirement already satisfied: typing-extensions>=4.14.1 in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from pydantic!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.*,!=2.4.*,<3->ray[tune]) (4.15.0)\n",
|
||
"Requirement already satisfied: typing-inspection>=0.4.2 in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from pydantic!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.*,!=2.4.*,<3->ray[tune]) (0.4.2)\n",
|
||
"Requirement already satisfied: colorama in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from click!=8.3.*,>=7.0->ray[tune]) (0.4.6)\n",
|
||
"Requirement already satisfied: numpy in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from tensorboardX>=1.9->ray[tune]) (2.3.5)\n",
|
||
"Requirement already satisfied: attrs>=22.2.0 in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from jsonschema->ray[tune]) (25.4.0)\n",
|
||
"Requirement already satisfied: jsonschema-specifications>=2023.03.6 in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from jsonschema->ray[tune]) (2025.9.1)\n",
|
||
"Requirement already satisfied: referencing>=0.28.4 in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from jsonschema->ray[tune]) (0.37.0)\n",
|
||
"Requirement already satisfied: rpds-py>=0.7.1 in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from jsonschema->ray[tune]) (0.30.0)\n",
|
||
"Requirement already satisfied: python-dateutil>=2.8.2 in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from pandas->ray[tune]) (2.9.0.post0)\n",
|
||
"Requirement already satisfied: pytz>=2020.1 in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from pandas->ray[tune]) (2025.2)\n",
|
||
"Requirement already satisfied: tzdata>=2022.7 in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from pandas->ray[tune]) (2025.2)\n",
|
||
"Requirement already satisfied: six>=1.5 in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from python-dateutil>=2.8.2->pandas->ray[tune]) (1.17.0)\n",
|
||
"Requirement already satisfied: charset_normalizer<4,>=2 in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from requests->ray[tune]) (3.4.4)\n",
|
||
"Requirement already satisfied: idna<4,>=2.5 in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from requests->ray[tune]) (3.11)\n",
|
||
"Requirement already satisfied: urllib3<3,>=1.21.1 in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from requests->ray[tune]) (2.6.0)\n",
|
||
"Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from requests->ray[tune]) (2025.11.12)\n",
|
||
"Note: you may need to restart the kernel to use updated packages.\n",
|
||
"Requirement already satisfied: optuna in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (4.6.0)\n",
|
||
"Requirement already satisfied: alembic>=1.5.0 in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from optuna) (1.17.2)\n",
|
||
"Requirement already satisfied: colorlog in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from optuna) (6.10.1)\n",
|
||
"Requirement already satisfied: numpy in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from optuna) (2.3.5)\n",
|
||
"Requirement already satisfied: packaging>=20.0 in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from optuna) (25.0)\n",
|
||
"Requirement already satisfied: sqlalchemy>=1.4.2 in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from optuna) (2.0.44)\n",
|
||
"Requirement already satisfied: tqdm in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from optuna) (4.67.1)\n",
|
||
"Requirement already satisfied: PyYAML in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from optuna) (6.0.2)\n",
|
||
"Requirement already satisfied: Mako in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from alembic>=1.5.0->optuna) (1.3.10)\n",
|
||
"Requirement already satisfied: typing-extensions>=4.12 in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from alembic>=1.5.0->optuna) (4.15.0)\n",
|
||
"Requirement already satisfied: greenlet>=1 in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from sqlalchemy>=1.4.2->optuna) (3.3.0)\n",
|
||
"Requirement already satisfied: colorama in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from colorlog->optuna) (0.4.6)\n",
|
||
"Requirement already satisfied: MarkupSafe>=0.9.2 in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from Mako->alembic>=1.5.0->optuna) (3.0.3)\n",
|
||
"Note: you may need to restart the kernel to use updated packages.\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# Instalación de Ray y Ray Tune\n",
|
||
"%pip install -U \"ray[tune]\" \n",
|
||
"%pip install optuna"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 6,
|
||
"id": "ae5a10c4",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"2025-12-07 19:58:07,710\tINFO worker.py:2023 -- Started a local Ray instance.\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Ray Tune listo (versión: 2.52.1 )\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"c:\\Users\\Sergio\\Desktop\\MastersThesis\\.venv\\Lib\\site-packages\\ray\\_private\\worker.py:2062: FutureWarning: Tip: In future versions of Ray, Ray will no longer override accelerator visible devices env var if num_gpus=0 or num_gpus=None (default). To enable this behavior and turn off this error message, set RAY_ACCEL_ENV_VAR_OVERRIDE_ON_ZERO=0\n",
|
||
" warnings.warn(\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# ===============================================================\n",
|
||
"# 🔍 RAY TUNE: OPTIMIZACIÓN AUTOMÁTICA DE HIPERPARÁMETROS OCR\n",
|
||
"# ===============================================================\n",
|
||
"import ray\n",
|
||
"from ray import tune, air\n",
|
||
"import pandas as pd\n",
|
||
"\n",
|
||
"\n",
|
||
"ray.init(ignore_reinit_error=True)\n",
|
||
"print(\"Ray Tune listo (versión:\", ray.__version__, \")\")\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 19,
|
||
"id": "96c320e8",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# --- Configuración base del experimento ---\n",
|
||
"search_space = {\n",
|
||
" #Whether to use document image orientation classification.\n",
|
||
" \"use_doc_orientation_classify\": tune.choice([True, False]), \n",
|
||
" # Whether to use text image unwarping.\n",
|
||
" \"use_doc_unwarping\": tune.choice([True, False]),\n",
|
||
" # Whether to use text line orientation classification.\n",
|
||
" \"textline_orientation\": tune.choice([True, False]),\n",
|
||
" # Detection pixel threshold for the text detection model. Pixels with scores greater than this threshold in the output probability map are considered text pixels.\n",
|
||
" \"text_det_thresh\" : tune.uniform(0.0, 0.7),\n",
|
||
" # Detection box threshold for the text detection model. A detection result is considered a text region if the average score of all pixels within the border of the result is greater than this threshold.\n",
|
||
" \"text_det_box_thresh\": tune.uniform(0.0, 0.7),\n",
|
||
" # Text detection expansion coefficient, which expands the text region using this method. The larger the value, the larger the expansion area.\n",
|
||
" \"text_det_unclip_ratio\": tune.choice([0.0]),\n",
|
||
" # Text recognition threshold. Text results with scores greater than this threshold are retained.\n",
|
||
" \"text_rec_score_thresh\": tune.uniform(0.0, 0.7),\n",
|
||
"}\n",
|
||
"KEYMAP = {\n",
|
||
" \"textline_orientation\": \"textline-orientation\",\n",
|
||
" \"use_doc_unwarping\": \"use-doc-unwarping\",\n",
|
||
" \"use_doc_orientation_classify\": \"use-doc-orientation-classify\",\n",
|
||
" \"text_det_box_thresh\": \"text-det-box-thresh\",\n",
|
||
" \"text_det_unclip_ratio\": \"text-det-unclip-ratio\",\n",
|
||
" \"text_rec_score_thresh\": \"text-rec-score-thresh\",\n",
|
||
" \"text_det_thresh\": \"text-det-thresh\"\n",
|
||
"}"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 20,
|
||
"id": "accb4e9d",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Notebook Python: c:\\Users\\Sergio\\Desktop\\MastersThesis\\.venv\\Scripts\\python.exe\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\u001b[36m(pid=gcs_server)\u001b[0m [2025-12-07 15:58:31,070 E 25184 15184] (gcs_server.exe) gcs_server.cc:303: Failed to establish connection to the event+metrics exporter agent. Events and metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14\n",
|
||
"\u001b[33m(raylet)\u001b[0m [2025-12-07 15:58:32,657 E 10072 20448] (raylet.exe) main.cc:979: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14\n",
|
||
"\u001b[36m(pid=18776)\u001b[0m [2025-12-07 15:58:36,373 E 18776 26484] core_worker_process.cc:837: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"{'CER': 0.012581110635031723, 'WER': 0.10406694286511942, 'TIME': 331.0908589363098, 'PAGES': 5, 'TIME_PER_PAGE': 66.11821403503419}\n",
|
||
"return code: 0\n",
|
||
"args: ['c:\\\\Users\\\\Sergio\\\\Desktop\\\\MastersThesis\\\\.venv\\\\Scripts\\\\python.exe', 'c:\\\\Users\\\\Sergio\\\\Desktop\\\\MastersThesis\\\\src\\\\paddle_ocr_tuning.py', '--pdf-folder', 'c:\\\\Users\\\\Sergio\\\\Desktop\\\\MastersThesis\\\\src\\\\dataset', '--textline-orientation', 'True', '--use-doc-unwarping', 'False', '--use-doc-orientation-classify', 'False', '--text-det-box-thresh', '0.0', '--text-det-unclip-ratio', '1.5', '--text-det-thresh', '0.0', '--text-rec-score-thresh', '0.0']\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"import sys, subprocess\n",
|
||
"print(\"Notebook Python:\", sys.executable)\n",
|
||
"# test paddle ocr run with params\n",
|
||
"args = [sys.executable, \n",
|
||
" SCRIPT_ABS, \n",
|
||
" \"--pdf-folder\", PDF_FOLDER_ABS, \n",
|
||
" \"--textline-orientation\",\"True\",\n",
|
||
" \"--use-doc-unwarping\",\"False\",\n",
|
||
" \"--use-doc-orientation-classify\",\"False\",\n",
|
||
" \"--text-det-box-thresh\",\"0.0\",\n",
|
||
" \"--text-det-unclip-ratio\",\"1.5\",\n",
|
||
" \"--text-det-thresh\", \"0.0\",\n",
|
||
" \"--text-rec-score-thresh\",\"0.0\"]\n",
|
||
"test_proc = subprocess.run(args, capture_output=True, text=True, cwd=SCRIPT_DIR)\n",
|
||
"if test_proc.returncode != 0:\n",
|
||
" print(test_proc.stderr)\n",
|
||
"last = test_proc.stdout.strip().splitlines()[-1]\n",
|
||
"\n",
|
||
"metrics = json.loads(last)\n",
|
||
"print(metrics)\n",
|
||
"\n",
|
||
"print(f\"return code: {test_proc.returncode}\")\n",
|
||
"print(f\"args: {args}\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 21,
|
||
"id": "8df28468",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"c:\\Users\\Sergio\\Desktop\\MastersThesis\\.venv\\Lib\\site-packages\\ray\\tune\\impl\\tuner_internal.py:144: RayDeprecationWarning: The `RunConfig` class should be imported from `ray.tune` when passing it to the Tuner. Please update your imports. See this issue for more context and migration options: https://github.com/ray-project/ray/issues/49454. Disable these warnings by setting the environment variable: RAY_TRAIN_ENABLE_V2_MIGRATION_WARNINGS=0\n",
|
||
" _log_deprecation_warning(\n",
|
||
"2025-12-07 16:03:56,654\tINFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n",
|
||
"[I 2025-12-07 16:03:56,662] A new study created in memory with name: optuna\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div class=\"tuneStatus\">\n",
|
||
" <div style=\"display: flex;flex-direction: row\">\n",
|
||
" <div style=\"display: flex;flex-direction: column;\">\n",
|
||
" <h3>Tune Status</h3>\n",
|
||
" <table>\n",
|
||
"<tbody>\n",
|
||
"<tr><td>Current time:</td><td>2025-12-07 19:23:17</td></tr>\n",
|
||
"<tr><td>Running for: </td><td>03:19:21.23 </td></tr>\n",
|
||
"<tr><td>Memory: </td><td>4.4/15.9 GiB </td></tr>\n",
|
||
"</tbody>\n",
|
||
"</table>\n",
|
||
" </div>\n",
|
||
" <div class=\"vDivider\"></div>\n",
|
||
" <div class=\"systemInfo\">\n",
|
||
" <h3>System Info</h3>\n",
|
||
" Using FIFO scheduling algorithm.<br>Logical resource usage: 1.0/16 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)\n",
|
||
" </div>\n",
|
||
" \n",
|
||
" </div>\n",
|
||
" <div class=\"hDivider\"></div>\n",
|
||
" <div class=\"trialStatus\">\n",
|
||
" <h3>Trial Status</h3>\n",
|
||
" <table>\n",
|
||
"<thead>\n",
|
||
"<tr><th>Trial name </th><th>status </th><th>loc </th><th style=\"text-align: right;\"> text_det_box_thresh</th><th style=\"text-align: right;\"> text_det_thresh</th><th style=\"text-align: right;\"> text_det_unclip_rati\n",
|
||
"o</th><th style=\"text-align: right;\"> text_rec_score_thres\n",
|
||
"h</th><th>textline_orientation </th><th>use_doc_orientation_\n",
|
||
"classify </th><th>use_doc_unwarping </th><th style=\"text-align: right;\"> iter</th><th style=\"text-align: right;\"> total time (s)</th><th style=\"text-align: right;\"> CER</th><th style=\"text-align: right;\"> WER</th><th style=\"text-align: right;\"> TIME</th></tr>\n",
|
||
"</thead>\n",
|
||
"<tbody>\n",
|
||
"<tr><td>trainable_paddle_ocr_d5238c33</td><td>TERMINATED</td><td>127.0.0.1:19452</td><td style=\"text-align: right;\"> 0.623029 </td><td style=\"text-align: right;\"> 0.0887821</td><td style=\"text-align: right;\">0</td><td style=\"text-align: right;\">0.229944 </td><td>True </td><td>True </td><td>False </td><td style=\"text-align: right;\"> 1</td><td style=\"text-align: right;\"> 374.278</td><td style=\"text-align: right;\">0.0135159</td><td style=\"text-align: right;\">0.105003 </td><td style=\"text-align: right;\">353.851</td></tr>\n",
|
||
"<tr><td>trainable_paddle_ocr_ea8a2f7a</td><td>TERMINATED</td><td>127.0.0.1:7472 </td><td style=\"text-align: right;\"> 0.671201 </td><td style=\"text-align: right;\"> 0.393201 </td><td style=\"text-align: right;\">0</td><td style=\"text-align: right;\">0.168802 </td><td>False </td><td>False</td><td>False </td><td style=\"text-align: right;\"> 1</td><td style=\"text-align: right;\"> 374.3 </td><td style=\"text-align: right;\">0.039052 </td><td style=\"text-align: right;\">0.132086 </td><td style=\"text-align: right;\">354.615</td></tr>\n",
|
||
"<tr><td>trainable_paddle_ocr_ebb12e5b</td><td>TERMINATED</td><td>127.0.0.1:21480</td><td style=\"text-align: right;\"> 0.235725 </td><td style=\"text-align: right;\"> 0.432878 </td><td style=\"text-align: right;\">0</td><td style=\"text-align: right;\">0.184435 </td><td>True </td><td>True </td><td>True </td><td style=\"text-align: right;\"> 1</td><td style=\"text-align: right;\"> 379.544</td><td style=\"text-align: right;\">0.0660624</td><td style=\"text-align: right;\">0.166192 </td><td style=\"text-align: right;\">359.097</td></tr>\n",
|
||
"<tr><td>trainable_paddle_ocr_b3775034</td><td>TERMINATED</td><td>127.0.0.1:23084</td><td style=\"text-align: right;\"> 0.337744 </td><td style=\"text-align: right;\"> 0.0641288</td><td style=\"text-align: right;\">0</td><td style=\"text-align: right;\">0.576405 </td><td>False </td><td>True </td><td>True </td><td style=\"text-align: right;\"> 1</td><td style=\"text-align: right;\"> 356.526</td><td style=\"text-align: right;\">0.418109 </td><td style=\"text-align: right;\">0.50371 </td><td style=\"text-align: right;\">336.661</td></tr>\n",
|
||
"<tr><td>trainable_paddle_ocr_bf10d370</td><td>TERMINATED</td><td>127.0.0.1:26140</td><td style=\"text-align: right;\"> 0.690232 </td><td style=\"text-align: right;\"> 0.671955 </td><td style=\"text-align: right;\">0</td><td style=\"text-align: right;\">0.39649 </td><td>True </td><td>True </td><td>True </td><td style=\"text-align: right;\"> 1</td><td style=\"text-align: right;\"> 370.903</td><td style=\"text-align: right;\">0.197252 </td><td style=\"text-align: right;\">0.295353 </td><td style=\"text-align: right;\">350.147</td></tr>\n",
|
||
"<tr><td>trainable_paddle_ocr_111e5a9e</td><td>TERMINATED</td><td>127.0.0.1:20664</td><td style=\"text-align: right;\"> 0.483266 </td><td style=\"text-align: right;\"> 0.044816 </td><td style=\"text-align: right;\">0</td><td style=\"text-align: right;\">0.546416 </td><td>False </td><td>True </td><td>False </td><td style=\"text-align: right;\"> 1</td><td style=\"text-align: right;\"> 341.071</td><td style=\"text-align: right;\">0.38641 </td><td style=\"text-align: right;\">0.455836 </td><td style=\"text-align: right;\">320.966</td></tr>\n",
|
||
"<tr><td>trainable_paddle_ocr_415d7ba1</td><td>TERMINATED</td><td>127.0.0.1:23848</td><td style=\"text-align: right;\"> 0.523385 </td><td style=\"text-align: right;\"> 0.0169971</td><td style=\"text-align: right;\">0</td><td style=\"text-align: right;\">0.208331 </td><td>True </td><td>True </td><td>True </td><td style=\"text-align: right;\"> 1</td><td style=\"text-align: right;\"> 347.299</td><td style=\"text-align: right;\">0.516069 </td><td style=\"text-align: right;\">0.59453 </td><td style=\"text-align: right;\">326.657</td></tr>\n",
|
||
"<tr><td>trainable_paddle_ocr_a58d8109</td><td>TERMINATED</td><td>127.0.0.1:25248</td><td style=\"text-align: right;\"> 0.670589 </td><td style=\"text-align: right;\"> 0.0402432</td><td style=\"text-align: right;\">0</td><td style=\"text-align: right;\">0.188585 </td><td>True </td><td>False</td><td>True </td><td style=\"text-align: right;\"> 1</td><td style=\"text-align: right;\"> 346.09 </td><td style=\"text-align: right;\">0.502513 </td><td style=\"text-align: right;\">0.567716 </td><td style=\"text-align: right;\">326.916</td></tr>\n",
|
||
"<tr><td>trainable_paddle_ocr_33bdf2a9</td><td>TERMINATED</td><td>127.0.0.1:24024</td><td style=\"text-align: right;\"> 0.490009 </td><td style=\"text-align: right;\"> 0.434737 </td><td style=\"text-align: right;\">0</td><td style=\"text-align: right;\">0.151906 </td><td>False </td><td>False</td><td>True </td><td style=\"text-align: right;\"> 1</td><td style=\"text-align: right;\"> 388.151</td><td style=\"text-align: right;\">0.0709203</td><td style=\"text-align: right;\">0.17391 </td><td style=\"text-align: right;\">368.571</td></tr>\n",
|
||
"<tr><td>trainable_paddle_ocr_d9df79f3</td><td>TERMINATED</td><td>127.0.0.1:5368 </td><td style=\"text-align: right;\"> 0.626194 </td><td style=\"text-align: right;\"> 0.178064 </td><td style=\"text-align: right;\">0</td><td style=\"text-align: right;\">0.385477 </td><td>False </td><td>True </td><td>True </td><td style=\"text-align: right;\"> 1</td><td style=\"text-align: right;\"> 384.677</td><td style=\"text-align: right;\">0.116825 </td><td style=\"text-align: right;\">0.22213 </td><td style=\"text-align: right;\">364.623</td></tr>\n",
|
||
"<tr><td>trainable_paddle_ocr_80ea65f2</td><td>TERMINATED</td><td>127.0.0.1:14064</td><td style=\"text-align: right;\"> 0.251382 </td><td style=\"text-align: right;\"> 0.601112 </td><td style=\"text-align: right;\">0</td><td style=\"text-align: right;\">0.313124 </td><td>False </td><td>True </td><td>True </td><td style=\"text-align: right;\"> 1</td><td style=\"text-align: right;\"> 387.679</td><td style=\"text-align: right;\">0.0645948</td><td style=\"text-align: right;\">0.164937 </td><td style=\"text-align: right;\">366.607</td></tr>\n",
|
||
"<tr><td>trainable_paddle_ocr_2e978bfa</td><td>TERMINATED</td><td>127.0.0.1:11060</td><td style=\"text-align: right;\"> 0.0777319 </td><td style=\"text-align: right;\"> 0.234859 </td><td style=\"text-align: right;\">0</td><td style=\"text-align: right;\">0.0236948 </td><td>True </td><td>False</td><td>False </td><td style=\"text-align: right;\"> 1</td><td style=\"text-align: right;\"> 380.281</td><td style=\"text-align: right;\">0.0134006</td><td style=\"text-align: right;\">0.107419 </td><td style=\"text-align: right;\">359.597</td></tr>\n",
|
||
"<tr><td>trainable_paddle_ocr_8518cc40</td><td>TERMINATED</td><td>127.0.0.1:21016</td><td style=\"text-align: right;\"> 0.000241868</td><td style=\"text-align: right;\"> 0.222556 </td><td style=\"text-align: right;\">0</td><td style=\"text-align: right;\">0.00289108</td><td>True </td><td>False</td><td>False </td><td style=\"text-align: right;\"> 1</td><td style=\"text-align: right;\"> 368.546</td><td style=\"text-align: right;\">0.0134006</td><td style=\"text-align: right;\">0.107419 </td><td style=\"text-align: right;\">347.929</td></tr>\n",
|
||
"<tr><td>trainable_paddle_ocr_2c691aaa</td><td>TERMINATED</td><td>127.0.0.1:21540</td><td style=\"text-align: right;\"> 0.0303334 </td><td style=\"text-align: right;\"> 0.224727 </td><td style=\"text-align: right;\">0</td><td style=\"text-align: right;\">0.0509969 </td><td>True </td><td>False</td><td>False </td><td style=\"text-align: right;\"> 1</td><td style=\"text-align: right;\"> 366.346</td><td style=\"text-align: right;\">0.0134006</td><td style=\"text-align: right;\">0.107419 </td><td style=\"text-align: right;\">347.145</td></tr>\n",
|
||
"<tr><td>trainable_paddle_ocr_31e60691</td><td>TERMINATED</td><td>127.0.0.1:17532</td><td style=\"text-align: right;\"> 0.00196041 </td><td style=\"text-align: right;\"> 0.259141 </td><td style=\"text-align: right;\">0</td><td style=\"text-align: right;\">0.00350944</td><td>True </td><td>False</td><td>False </td><td style=\"text-align: right;\"> 1</td><td style=\"text-align: right;\"> 368.038</td><td style=\"text-align: right;\">0.0130404</td><td style=\"text-align: right;\">0.104854 </td><td style=\"text-align: right;\">347.22 </td></tr>\n",
|
||
"<tr><td>trainable_paddle_ocr_d4d288c6</td><td>TERMINATED</td><td>127.0.0.1:22216</td><td style=\"text-align: right;\"> 0.00339892 </td><td style=\"text-align: right;\"> 0.273408 </td><td style=\"text-align: right;\">0</td><td style=\"text-align: right;\">0.0154205 </td><td>True </td><td>False</td><td>False </td><td style=\"text-align: right;\"> 1</td><td style=\"text-align: right;\"> 368.904</td><td style=\"text-align: right;\">0.0125829</td><td style=\"text-align: right;\">0.10328 </td><td style=\"text-align: right;\">349.232</td></tr>\n",
|
||
"<tr><td>trainable_paddle_ocr_7645b77c</td><td>TERMINATED</td><td>127.0.0.1:2272 </td><td style=\"text-align: right;\"> 0.113841 </td><td style=\"text-align: right;\"> 0.279242 </td><td style=\"text-align: right;\">0</td><td style=\"text-align: right;\">0.0753151 </td><td>True </td><td>False</td><td>False </td><td style=\"text-align: right;\"> 1</td><td style=\"text-align: right;\"> 367.456</td><td style=\"text-align: right;\">0.0125829</td><td style=\"text-align: right;\">0.10328 </td><td style=\"text-align: right;\">346.698</td></tr>\n",
|
||
"<tr><td>trainable_paddle_ocr_3256ae36</td><td>TERMINATED</td><td>127.0.0.1:6604 </td><td style=\"text-align: right;\"> 0.129213 </td><td style=\"text-align: right;\"> 0.30993 </td><td style=\"text-align: right;\">0</td><td style=\"text-align: right;\">0.11202 </td><td>True </td><td>False</td><td>False </td><td style=\"text-align: right;\"> 1</td><td style=\"text-align: right;\"> 366.002</td><td style=\"text-align: right;\">0.0124076</td><td style=\"text-align: right;\">0.102016 </td><td style=\"text-align: right;\">346.52 </td></tr>\n",
|
||
"<tr><td>trainable_paddle_ocr_b0dda58b</td><td>TERMINATED</td><td>127.0.0.1:9732 </td><td style=\"text-align: right;\"> 0.117838 </td><td style=\"text-align: right;\"> 0.314952 </td><td style=\"text-align: right;\">0</td><td style=\"text-align: right;\">0.682573 </td><td>True </td><td>False</td><td>False </td><td style=\"text-align: right;\"> 1</td><td style=\"text-align: right;\"> 364.828</td><td style=\"text-align: right;\">0.0124076</td><td style=\"text-align: right;\">0.102016 </td><td style=\"text-align: right;\">344.029</td></tr>\n",
|
||
"<tr><td>trainable_paddle_ocr_e9d40333</td><td>TERMINATED</td><td>127.0.0.1:23416</td><td style=\"text-align: right;\"> 0.156939 </td><td style=\"text-align: right;\"> 0.530252 </td><td style=\"text-align: right;\">0</td><td style=\"text-align: right;\">0.100194 </td><td>True </td><td>False</td><td>False </td><td style=\"text-align: right;\"> 1</td><td style=\"text-align: right;\"> 365.626</td><td style=\"text-align: right;\">0.0124298</td><td style=\"text-align: right;\">0.102051 </td><td style=\"text-align: right;\">346.118</td></tr>\n",
|
||
"<tr><td>trainable_paddle_ocr_aa89fe7a</td><td>TERMINATED</td><td>127.0.0.1:16200</td><td style=\"text-align: right;\"> 0.162083 </td><td style=\"text-align: right;\"> 0.50397 </td><td style=\"text-align: right;\">0</td><td style=\"text-align: right;\">0.676539 </td><td>True </td><td>False</td><td>False </td><td style=\"text-align: right;\"> 1</td><td style=\"text-align: right;\"> 366.753</td><td style=\"text-align: right;\">0.0119907</td><td style=\"text-align: right;\">0.100476 </td><td style=\"text-align: right;\">346.54 </td></tr>\n",
|
||
"<tr><td>trainable_paddle_ocr_92c48d07</td><td>TERMINATED</td><td>127.0.0.1:15432</td><td style=\"text-align: right;\"> 0.186443 </td><td style=\"text-align: right;\"> 0.333219 </td><td style=\"text-align: right;\">0</td><td style=\"text-align: right;\">0.67753 </td><td>True </td><td>False</td><td>False </td><td style=\"text-align: right;\"> 1</td><td style=\"text-align: right;\"> 365.094</td><td style=\"text-align: right;\">0.0119685</td><td style=\"text-align: right;\">0.100441 </td><td style=\"text-align: right;\">345.979</td></tr>\n",
|
||
"<tr><td>trainable_paddle_ocr_187790d7</td><td>TERMINATED</td><td>127.0.0.1:24676</td><td style=\"text-align: right;\"> 0.235252 </td><td style=\"text-align: right;\"> 0.337251 </td><td style=\"text-align: right;\">0</td><td style=\"text-align: right;\">0.698732 </td><td>True </td><td>False</td><td>False </td><td style=\"text-align: right;\"> 1</td><td style=\"text-align: right;\"> 364.474</td><td style=\"text-align: right;\">0.0119685</td><td style=\"text-align: right;\">0.100441 </td><td style=\"text-align: right;\">344.173</td></tr>\n",
|
||
"<tr><td>trainable_paddle_ocr_442a2439</td><td>TERMINATED</td><td>127.0.0.1:7892 </td><td style=\"text-align: right;\"> 0.212276 </td><td style=\"text-align: right;\"> 0.509804 </td><td style=\"text-align: right;\">0</td><td style=\"text-align: right;\">0.699247 </td><td>True </td><td>False</td><td>False </td><td style=\"text-align: right;\"> 1</td><td style=\"text-align: right;\"> 364.755</td><td style=\"text-align: right;\">0.0117601</td><td style=\"text-align: right;\">0.0996499</td><td style=\"text-align: right;\">345.943</td></tr>\n",
|
||
"<tr><td>trainable_paddle_ocr_70862adc</td><td>TERMINATED</td><td>127.0.0.1:15412</td><td style=\"text-align: right;\"> 0.216306 </td><td style=\"text-align: right;\"> 0.396397 </td><td style=\"text-align: right;\">0</td><td style=\"text-align: right;\">0.685918 </td><td>True </td><td>False</td><td>False </td><td style=\"text-align: right;\"> 1</td><td style=\"text-align: right;\"> 365.975</td><td style=\"text-align: right;\">0.0119685</td><td style=\"text-align: right;\">0.100441 </td><td style=\"text-align: right;\">345.403</td></tr>\n",
|
||
"<tr><td>trainable_paddle_ocr_e6821f34</td><td>TERMINATED</td><td>127.0.0.1:26088</td><td style=\"text-align: right;\"> 0.240775 </td><td style=\"text-align: right;\"> 0.366898 </td><td style=\"text-align: right;\">0</td><td style=\"text-align: right;\">0.573762 </td><td>True </td><td>False</td><td>False </td><td style=\"text-align: right;\"> 1</td><td style=\"text-align: right;\"> 365.255</td><td style=\"text-align: right;\">0.0124076</td><td style=\"text-align: right;\">0.102016 </td><td style=\"text-align: right;\">345.881</td></tr>\n",
|
||
"<tr><td>trainable_paddle_ocr_8b680875</td><td>TERMINATED</td><td>127.0.0.1:1720 </td><td style=\"text-align: right;\"> 0.319343 </td><td style=\"text-align: right;\"> 0.53125 </td><td style=\"text-align: right;\">0</td><td style=\"text-align: right;\">0.591253 </td><td>True </td><td>False</td><td>False </td><td style=\"text-align: right;\"> 1</td><td style=\"text-align: right;\"> 367.203</td><td style=\"text-align: right;\">0.0121992</td><td style=\"text-align: right;\">0.101225 </td><td style=\"text-align: right;\">347.056</td></tr>\n",
|
||
"<tr><td>trainable_paddle_ocr_fc54867b</td><td>TERMINATED</td><td>127.0.0.1:4888 </td><td style=\"text-align: right;\"> 0.304286 </td><td style=\"text-align: right;\"> 0.503408 </td><td style=\"text-align: right;\">0</td><td style=\"text-align: right;\">0.502491 </td><td>True </td><td>False</td><td>False </td><td style=\"text-align: right;\"> 1</td><td style=\"text-align: right;\"> 368.736</td><td style=\"text-align: right;\">0.0124298</td><td style=\"text-align: right;\">0.102051 </td><td style=\"text-align: right;\">349.607</td></tr>\n",
|
||
"<tr><td>trainable_paddle_ocr_c32d0d5e</td><td>TERMINATED</td><td>127.0.0.1:25808</td><td style=\"text-align: right;\"> 0.398489 </td><td style=\"text-align: right;\"> 0.153007 </td><td style=\"text-align: right;\">0</td><td style=\"text-align: right;\">0.516768 </td><td>True </td><td>False</td><td>False </td><td style=\"text-align: right;\"> 1</td><td style=\"text-align: right;\"> 364.423</td><td style=\"text-align: right;\">0.0133855</td><td style=\"text-align: right;\">0.109273 </td><td style=\"text-align: right;\">343.855</td></tr>\n",
|
||
"<tr><td>trainable_paddle_ocr_4762fbbb</td><td>TERMINATED</td><td>127.0.0.1:20760</td><td style=\"text-align: right;\"> 0.40101 </td><td style=\"text-align: right;\"> 0.133426 </td><td style=\"text-align: right;\">0</td><td style=\"text-align: right;\">0.618812 </td><td>True </td><td>False</td><td>False </td><td style=\"text-align: right;\"> 1</td><td style=\"text-align: right;\"> 363.326</td><td style=\"text-align: right;\">0.0135372</td><td style=\"text-align: right;\">0.108525 </td><td style=\"text-align: right;\">344.601</td></tr>\n",
|
||
"<tr><td>trainable_paddle_ocr_522ac97c</td><td>TERMINATED</td><td>127.0.0.1:2372 </td><td style=\"text-align: right;\"> 0.402755 </td><td style=\"text-align: right;\"> 0.448976 </td><td style=\"text-align: right;\">0</td><td style=\"text-align: right;\">0.642637 </td><td>True </td><td>False</td><td>False </td><td style=\"text-align: right;\"> 1</td><td style=\"text-align: right;\"> 364.72 </td><td style=\"text-align: right;\">0.0117638</td><td style=\"text-align: right;\">0.099689 </td><td style=\"text-align: right;\">344.038</td></tr>\n",
|
||
"<tr><td>trainable_paddle_ocr_5784f433</td><td>TERMINATED</td><td>127.0.0.1:22900</td><td style=\"text-align: right;\"> 0.192769 </td><td style=\"text-align: right;\"> 0.46205 </td><td style=\"text-align: right;\">0</td><td style=\"text-align: right;\">0.632828 </td><td>True </td><td>False</td><td>False </td><td style=\"text-align: right;\"> 1</td><td style=\"text-align: right;\"> 362.93 </td><td style=\"text-align: right;\">0.0116503</td><td style=\"text-align: right;\">0.0989016</td><td style=\"text-align: right;\">343.513</td></tr>\n",
|
||
"<tr><td>trainable_paddle_ocr_83af0528</td><td>TERMINATED</td><td>127.0.0.1:9832 </td><td style=\"text-align: right;\"> 0.184587 </td><td style=\"text-align: right;\"> 0.466314 </td><td style=\"text-align: right;\">0</td><td style=\"text-align: right;\">0.629921 </td><td>True </td><td>False</td><td>False </td><td style=\"text-align: right;\"> 1</td><td style=\"text-align: right;\"> 364.585</td><td style=\"text-align: right;\">0.0116503</td><td style=\"text-align: right;\">0.0989016</td><td style=\"text-align: right;\">343.81 </td></tr>\n",
|
||
"<tr><td>trainable_paddle_ocr_12cbaa22</td><td>TERMINATED</td><td>127.0.0.1:5968 </td><td style=\"text-align: right;\"> 0.405622 </td><td style=\"text-align: right;\"> 0.472779 </td><td style=\"text-align: right;\">0</td><td style=\"text-align: right;\">0.631499 </td><td>True </td><td>False</td><td>False </td><td style=\"text-align: right;\"> 1</td><td style=\"text-align: right;\"> 364.247</td><td style=\"text-align: right;\">0.0116503</td><td style=\"text-align: right;\">0.0989016</td><td style=\"text-align: right;\">344.114</td></tr>\n",
|
||
"<tr><td>trainable_paddle_ocr_a3a87765</td><td>TERMINATED</td><td>127.0.0.1:24372</td><td style=\"text-align: right;\"> 0.28557 </td><td style=\"text-align: right;\"> 0.4501 </td><td style=\"text-align: right;\">0</td><td style=\"text-align: right;\">0.635152 </td><td>True </td><td>False</td><td>False </td><td style=\"text-align: right;\"> 1</td><td style=\"text-align: right;\"> 369.274</td><td style=\"text-align: right;\">0.0117638</td><td style=\"text-align: right;\">0.099689 </td><td style=\"text-align: right;\">348.58 </td></tr>\n",
|
||
"<tr><td>trainable_paddle_ocr_cf2bad0c</td><td>TERMINATED</td><td>127.0.0.1:3272 </td><td style=\"text-align: right;\"> 0.283661 </td><td style=\"text-align: right;\"> 0.589012 </td><td style=\"text-align: right;\">0</td><td style=\"text-align: right;\">0.460291 </td><td>False </td><td>False</td><td>False </td><td style=\"text-align: right;\"> 1</td><td style=\"text-align: right;\"> 366.188</td><td style=\"text-align: right;\">0.044199 </td><td style=\"text-align: right;\">0.132047 </td><td style=\"text-align: right;\">347.034</td></tr>\n",
|
||
"<tr><td>trainable_paddle_ocr_9a9b91e7</td><td>TERMINATED</td><td>127.0.0.1:2272 </td><td style=\"text-align: right;\"> 0.364609 </td><td style=\"text-align: right;\"> 0.608959 </td><td style=\"text-align: right;\">0</td><td style=\"text-align: right;\">0.465225 </td><td>False </td><td>False</td><td>False </td><td style=\"text-align: right;\"> 1</td><td style=\"text-align: right;\"> 364.017</td><td style=\"text-align: right;\">0.044199 </td><td style=\"text-align: right;\">0.132047 </td><td style=\"text-align: right;\">343.539</td></tr>\n",
|
||
"<tr><td>trainable_paddle_ocr_e326d901</td><td>TERMINATED</td><td>127.0.0.1:24932</td><td style=\"text-align: right;\"> 0.373537 </td><td style=\"text-align: right;\"> 0.593229 </td><td style=\"text-align: right;\">0</td><td style=\"text-align: right;\">0.463688 </td><td>True </td><td>False</td><td>False </td><td style=\"text-align: right;\"> 1</td><td style=\"text-align: right;\"> 365.428</td><td style=\"text-align: right;\">0.0121992</td><td style=\"text-align: right;\">0.101225 </td><td style=\"text-align: right;\">345.762</td></tr>\n",
|
||
"<tr><td>trainable_paddle_ocr_ccb3f19a</td><td>TERMINATED</td><td>127.0.0.1:1104 </td><td style=\"text-align: right;\"> 0.453777 </td><td style=\"text-align: right;\"> 0.686641 </td><td style=\"text-align: right;\">0</td><td style=\"text-align: right;\">0.305928 </td><td>True </td><td>True </td><td>False </td><td style=\"text-align: right;\"> 1</td><td style=\"text-align: right;\"> 365.147</td><td style=\"text-align: right;\">0.0119903</td><td style=\"text-align: right;\">0.0991043</td><td style=\"text-align: right;\">344.408</td></tr>\n",
|
||
"<tr><td>trainable_paddle_ocr_8c12c55f</td><td>TERMINATED</td><td>127.0.0.1:19700</td><td style=\"text-align: right;\"> 0.444416 </td><td style=\"text-align: right;\"> 0.67104 </td><td style=\"text-align: right;\">0</td><td style=\"text-align: right;\">0.264132 </td><td>True </td><td>True </td><td>False </td><td style=\"text-align: right;\"> 1</td><td style=\"text-align: right;\"> 363.297</td><td style=\"text-align: right;\">0.0121862</td><td style=\"text-align: right;\">0.101228 </td><td style=\"text-align: right;\">343.939</td></tr>\n",
|
||
"<tr><td>trainable_paddle_ocr_5a62d5b6</td><td>TERMINATED</td><td>127.0.0.1:26528</td><td style=\"text-align: right;\"> 0.201047 </td><td style=\"text-align: right;\"> 0.404141 </td><td style=\"text-align: right;\">0</td><td style=\"text-align: right;\">0.599257 </td><td>True </td><td>True </td><td>True </td><td style=\"text-align: right;\"> 1</td><td style=\"text-align: right;\"> 380.333</td><td style=\"text-align: right;\">0.0662709</td><td style=\"text-align: right;\">0.168515 </td><td style=\"text-align: right;\">359.467</td></tr>\n",
|
||
"<tr><td>trainable_paddle_ocr_bb4495b7</td><td>TERMINATED</td><td>127.0.0.1:21772</td><td style=\"text-align: right;\"> 0.576439 </td><td style=\"text-align: right;\"> 0.390737 </td><td style=\"text-align: right;\">0</td><td style=\"text-align: right;\">0.541396 </td><td>False </td><td>False</td><td>True </td><td style=\"text-align: right;\"> 1</td><td style=\"text-align: right;\"> 375.977</td><td style=\"text-align: right;\">0.0707008</td><td style=\"text-align: right;\">0.17391 </td><td style=\"text-align: right;\">356.322</td></tr>\n",
|
||
"<tr><td>trainable_paddle_ocr_9d90711d</td><td>TERMINATED</td><td>127.0.0.1:17592</td><td style=\"text-align: right;\"> 0.541158 </td><td style=\"text-align: right;\"> 0.468954 </td><td style=\"text-align: right;\">0</td><td style=\"text-align: right;\">0.635015 </td><td>True </td><td>False</td><td>False </td><td style=\"text-align: right;\"> 1</td><td style=\"text-align: right;\"> 365.77 </td><td style=\"text-align: right;\">0.0115351</td><td style=\"text-align: right;\">0.0989016</td><td style=\"text-align: right;\">344.718</td></tr>\n",
|
||
"<tr><td>trainable_paddle_ocr_daaec3f8</td><td>TERMINATED</td><td>127.0.0.1:21292</td><td style=\"text-align: right;\"> 0.521341 </td><td style=\"text-align: right;\"> 0.474351 </td><td style=\"text-align: right;\">0</td><td style=\"text-align: right;\">0.644567 </td><td>True </td><td>False</td><td>False </td><td style=\"text-align: right;\"> 1</td><td style=\"text-align: right;\"> 363.019</td><td style=\"text-align: right;\">0.0115351</td><td style=\"text-align: right;\">0.0989016</td><td style=\"text-align: right;\">343.697</td></tr>\n",
|
||
"<tr><td>trainable_paddle_ocr_51fb5915</td><td>TERMINATED</td><td>127.0.0.1:21772</td><td style=\"text-align: right;\"> 0.58105 </td><td style=\"text-align: right;\"> 0.485412 </td><td style=\"text-align: right;\">0</td><td style=\"text-align: right;\">0.64636 </td><td>True </td><td>False</td><td>False </td><td style=\"text-align: right;\"> 1</td><td style=\"text-align: right;\"> 364.02 </td><td style=\"text-align: right;\">0.0115351</td><td style=\"text-align: right;\">0.0989016</td><td style=\"text-align: right;\">343.604</td></tr>\n",
|
||
"<tr><td>trainable_paddle_ocr_18966a33</td><td>TERMINATED</td><td>127.0.0.1:16900</td><td style=\"text-align: right;\"> 0.51329 </td><td style=\"text-align: right;\"> 0.550159 </td><td style=\"text-align: right;\">0</td><td style=\"text-align: right;\">0.648982 </td><td>True </td><td>False</td><td>False </td><td style=\"text-align: right;\"> 1</td><td style=\"text-align: right;\"> 363.337</td><td style=\"text-align: right;\">0.0116449</td><td style=\"text-align: right;\">0.0996499</td><td style=\"text-align: right;\">344.261</td></tr>\n",
|
||
"<tr><td>trainable_paddle_ocr_b67080f9</td><td>TERMINATED</td><td>127.0.0.1:20948</td><td style=\"text-align: right;\"> 0.576074 </td><td style=\"text-align: right;\"> 0.553412 </td><td style=\"text-align: right;\">0</td><td style=\"text-align: right;\">0.560972 </td><td>True </td><td>False</td><td>False </td><td style=\"text-align: right;\"> 1</td><td style=\"text-align: right;\"> 366.019</td><td style=\"text-align: right;\">0.0123145</td><td style=\"text-align: right;\">0.102051 </td><td style=\"text-align: right;\">345.495</td></tr>\n",
|
||
"<tr><td>trainable_paddle_ocr_2533f368</td><td>TERMINATED</td><td>127.0.0.1:11208</td><td style=\"text-align: right;\"> 0.524608 </td><td style=\"text-align: right;\"> 0.557227 </td><td style=\"text-align: right;\">0</td><td style=\"text-align: right;\">0.558307 </td><td>True </td><td>False</td><td>True </td><td style=\"text-align: right;\"> 1</td><td style=\"text-align: right;\"> 371.205</td><td style=\"text-align: right;\">0.0720912</td><td style=\"text-align: right;\">0.179189 </td><td style=\"text-align: right;\">351.967</td></tr>\n",
|
||
"<tr><td>trainable_paddle_ocr_451d018d</td><td>TERMINATED</td><td>127.0.0.1:3616 </td><td style=\"text-align: right;\"> 0.549464 </td><td style=\"text-align: right;\"> 0.634019 </td><td style=\"text-align: right;\">0</td><td style=\"text-align: right;\">0.652105 </td><td>False </td><td>False</td><td>True </td><td style=\"text-align: right;\"> 1</td><td style=\"text-align: right;\"> 378.827</td><td style=\"text-align: right;\">0.0647995</td><td style=\"text-align: right;\">0.164937 </td><td style=\"text-align: right;\">357.17 </td></tr>\n",
|
||
"<tr><td>trainable_paddle_ocr_2256e752</td><td>TERMINATED</td><td>127.0.0.1:25468</td><td style=\"text-align: right;\"> 0.622863 </td><td style=\"text-align: right;\"> 0.647804 </td><td style=\"text-align: right;\">0</td><td style=\"text-align: right;\">0.654609 </td><td>False </td><td>True </td><td>False </td><td style=\"text-align: right;\"> 1</td><td style=\"text-align: right;\"> 369.88 </td><td style=\"text-align: right;\">0.0442921</td><td style=\"text-align: right;\">0.132838 </td><td style=\"text-align: right;\">349.417</td></tr>\n",
|
||
"<tr><td>trainable_paddle_ocr_0a892729</td><td>TERMINATED</td><td>127.0.0.1:26212</td><td style=\"text-align: right;\"> 0.542929 </td><td style=\"text-align: right;\"> 0.421733 </td><td style=\"text-align: right;\">0</td><td style=\"text-align: right;\">0.601587 </td><td>True </td><td>False</td><td>False </td><td style=\"text-align: right;\"> 1</td><td style=\"text-align: right;\"> 367.237</td><td style=\"text-align: right;\">0.0122923</td><td style=\"text-align: right;\">0.102016 </td><td style=\"text-align: right;\">346.072</td></tr>\n",
|
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"<tr><td>trainable_paddle_ocr_495075f5</td><td>TERMINATED</td><td>127.0.0.1:23604</td><td style=\"text-align: right;\"> 0.631875 </td><td style=\"text-align: right;\"> 0.418675 </td><td style=\"text-align: right;\">0</td><td style=\"text-align: right;\">0.595618 </td><td>True </td><td>False</td><td>False </td><td style=\"text-align: right;\"> 1</td><td style=\"text-align: right;\"> 365.536</td><td style=\"text-align: right;\">0.0122923</td><td style=\"text-align: right;\">0.102016 </td><td style=\"text-align: right;\">346.425</td></tr>\n",
|
||
"<tr><td>trainable_paddle_ocr_54c45552</td><td>TERMINATED</td><td>127.0.0.1:25352</td><td style=\"text-align: right;\"> 0.619687 </td><td style=\"text-align: right;\"> 0.463823 </td><td style=\"text-align: right;\">0</td><td style=\"text-align: right;\">0.612612 </td><td>True </td><td>False</td><td>False </td><td style=\"text-align: right;\"> 1</td><td style=\"text-align: right;\"> 367.947</td><td style=\"text-align: right;\">0.0119742</td><td style=\"text-align: right;\">0.100476 </td><td style=\"text-align: right;\">346.941</td></tr>\n",
|
||
"<tr><td>trainable_paddle_ocr_6b2e9b93</td><td>TERMINATED</td><td>127.0.0.1:25400</td><td style=\"text-align: right;\"> 0.48925 </td><td style=\"text-align: right;\"> 0.475185 </td><td style=\"text-align: right;\">0</td><td style=\"text-align: right;\">0.515482 </td><td>True </td><td>False</td><td>False </td><td style=\"text-align: right;\"> 1</td><td style=\"text-align: right;\"> 365.989</td><td style=\"text-align: right;\">0.0119742</td><td style=\"text-align: right;\">0.100476 </td><td style=\"text-align: right;\">346.414</td></tr>\n",
|
||
"<tr><td>trainable_paddle_ocr_e9a6b81f</td><td>TERMINATED</td><td>127.0.0.1:4036 </td><td style=\"text-align: right;\"> 0.492552 </td><td style=\"text-align: right;\"> 0.48793 </td><td style=\"text-align: right;\">0</td><td style=\"text-align: right;\">0.648349 </td><td>True </td><td>False</td><td>False </td><td style=\"text-align: right;\"> 1</td><td style=\"text-align: right;\"> 367.332</td><td style=\"text-align: right;\">0.0115351</td><td style=\"text-align: right;\">0.0989016</td><td style=\"text-align: right;\">346.259</td></tr>\n",
|
||
"<tr><td>trainable_paddle_ocr_076c5450</td><td>TERMINATED</td><td>127.0.0.1:4832 </td><td style=\"text-align: right;\"> 0.588133 </td><td style=\"text-align: right;\"> 0.488422 </td><td style=\"text-align: right;\">0</td><td style=\"text-align: right;\">0.656919 </td><td>True </td><td>False</td><td>False </td><td style=\"text-align: right;\"> 1</td><td style=\"text-align: right;\"> 365.188</td><td style=\"text-align: right;\">0.0115351</td><td style=\"text-align: right;\">0.0989016</td><td style=\"text-align: right;\">345.843</td></tr>\n",
|
||
"<tr><td>trainable_paddle_ocr_4a42a3ea</td><td>TERMINATED</td><td>127.0.0.1:14912</td><td style=\"text-align: right;\"> 0.594041 </td><td style=\"text-align: right;\"> 0.559036 </td><td style=\"text-align: right;\">0</td><td style=\"text-align: right;\">0.657323 </td><td>True </td><td>False</td><td>False </td><td style=\"text-align: right;\"> 1</td><td style=\"text-align: right;\"> 370.997</td><td style=\"text-align: right;\">0.0118754</td><td style=\"text-align: right;\">0.100476 </td><td style=\"text-align: right;\">350.244</td></tr>\n",
|
||
"<tr><td>trainable_paddle_ocr_041795f1</td><td>TERMINATED</td><td>127.0.0.1:22372</td><td style=\"text-align: right;\"> 0.661744 </td><td style=\"text-align: right;\"> 0.565009 </td><td style=\"text-align: right;\">0</td><td style=\"text-align: right;\">0.66295 </td><td>True </td><td>False</td><td>False </td><td style=\"text-align: right;\"> 1</td><td style=\"text-align: right;\"> 370.946</td><td style=\"text-align: right;\">0.0120801</td><td style=\"text-align: right;\">0.100476 </td><td style=\"text-align: right;\">351.5 </td></tr>\n",
|
||
"<tr><td>trainable_paddle_ocr_8abb3f37</td><td>TERMINATED</td><td>127.0.0.1:22012</td><td style=\"text-align: right;\"> 0.463682 </td><td style=\"text-align: right;\"> 0.489821 </td><td style=\"text-align: right;\">0</td><td style=\"text-align: right;\">0.394583 </td><td>True </td><td>False</td><td>False </td><td style=\"text-align: right;\"> 1</td><td style=\"text-align: right;\"> 364.675</td><td style=\"text-align: right;\">0.0123145</td><td style=\"text-align: right;\">0.102051 </td><td style=\"text-align: right;\">343.539</td></tr>\n",
|
||
"<tr><td>trainable_paddle_ocr_f2cb682e</td><td>TERMINATED</td><td>127.0.0.1:5752 </td><td style=\"text-align: right;\"> 0.452248 </td><td style=\"text-align: right;\"> 0.491795 </td><td style=\"text-align: right;\">0</td><td style=\"text-align: right;\">0.425971 </td><td>True </td><td>True </td><td>False </td><td style=\"text-align: right;\"> 1</td><td style=\"text-align: right;\"> 364.908</td><td style=\"text-align: right;\">0.0123145</td><td style=\"text-align: right;\">0.102051 </td><td style=\"text-align: right;\">345.592</td></tr>\n",
|
||
"<tr><td>trainable_paddle_ocr_463fe5e7</td><td>TERMINATED</td><td>127.0.0.1:16524</td><td style=\"text-align: right;\"> 0.520238 </td><td style=\"text-align: right;\"> 0.537344 </td><td style=\"text-align: right;\">0</td><td style=\"text-align: right;\">0.534057 </td><td>True </td><td>True </td><td>False </td><td style=\"text-align: right;\"> 1</td><td style=\"text-align: right;\"> 370.564</td><td style=\"text-align: right;\">0.0123145</td><td style=\"text-align: right;\">0.102051 </td><td style=\"text-align: right;\">349.509</td></tr>\n",
|
||
"<tr><td>trainable_paddle_ocr_88bbe87d</td><td>TERMINATED</td><td>127.0.0.1:15084</td><td style=\"text-align: right;\"> 0.511078 </td><td style=\"text-align: right;\"> 0.527459 </td><td style=\"text-align: right;\">0</td><td style=\"text-align: right;\">0.536896 </td><td>True </td><td>False</td><td>False </td><td style=\"text-align: right;\"> 1</td><td style=\"text-align: right;\"> 369.55 </td><td style=\"text-align: right;\">0.0120839</td><td style=\"text-align: right;\">0.101225 </td><td style=\"text-align: right;\">350.144</td></tr>\n",
|
||
"<tr><td>trainable_paddle_ocr_33ea1cc6</td><td>TERMINATED</td><td>127.0.0.1:17380</td><td style=\"text-align: right;\"> 0.515807 </td><td style=\"text-align: right;\"> 0.522992 </td><td style=\"text-align: right;\">0</td><td style=\"text-align: right;\">0.667966 </td><td>True </td><td>False</td><td>False </td><td style=\"text-align: right;\"> 1</td><td style=\"text-align: right;\"> 376.746</td><td style=\"text-align: right;\">0.0118754</td><td style=\"text-align: right;\">0.100476 </td><td style=\"text-align: right;\">355.524</td></tr>\n",
|
||
"<tr><td>trainable_paddle_ocr_1243723e</td><td>TERMINATED</td><td>127.0.0.1:11232</td><td style=\"text-align: right;\"> 0.557315 </td><td style=\"text-align: right;\"> 0.372677 </td><td style=\"text-align: right;\">0</td><td style=\"text-align: right;\">0.676613 </td><td>True </td><td>False</td><td>False </td><td style=\"text-align: right;\"> 1</td><td style=\"text-align: right;\"> 375.444</td><td style=\"text-align: right;\">0.0118532</td><td style=\"text-align: right;\">0.100441 </td><td style=\"text-align: right;\">355.679</td></tr>\n",
|
||
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|
||
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|
||
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|
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|
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|
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|
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|
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|
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|
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|
||
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|
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|
||
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|
||
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|
||
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|
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|
||
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|
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|
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|
||
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|
||
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|
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|
||
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|
||
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|
||
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|
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|
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|
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|
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|
||
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|
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|
||
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|
||
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|
||
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|
||
"2025-12-07 16:03:56,713\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_d5238c33_1_text_det_box_thresh=0.6230,text_det_thresh=0.0888,text_det_unclip_ratio=0.0000,text_rec_score_thre_2025-12-07_16-03-56\n",
|
||
"2025-12-07 16:03:56,718\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_d5238c33_1_text_det_box_thresh=0.6230,text_det_thresh=0.0888,text_det_unclip_ratio=0.0000,text_rec_score_thre_2025-12-07_16-03-56\n",
|
||
"2025-12-07 16:04:01,625\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_d5238c33_1_text_det_box_thresh=0.6230,text_det_thresh=0.0888,text_det_unclip_ratio=0.0000,text_rec_score_thre_2025-12-07_16-03-56\n",
|
||
"2025-12-07 16:04:01,626\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_d5238c33_1_text_det_box_thresh=0.6230,text_det_thresh=0.0888,text_det_unclip_ratio=0.0000,text_rec_score_thre_2025-12-07_16-03-56\n",
|
||
"2025-12-07 16:04:01,639\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_ea8a2f7a_2_text_det_box_thresh=0.6712,text_det_thresh=0.3932,text_det_unclip_ratio=0.0000,text_rec_score_thre_2025-12-07_16-04-01\n",
|
||
"2025-12-07 16:04:01,642\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_ea8a2f7a_2_text_det_box_thresh=0.6712,text_det_thresh=0.3932,text_det_unclip_ratio=0.0000,text_rec_score_thre_2025-12-07_16-04-01\n",
|
||
"2025-12-07 16:04:06,097\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_ea8a2f7a_2_text_det_box_thresh=0.6712,text_det_thresh=0.3932,text_det_unclip_ratio=0.0000,text_rec_score_thre_2025-12-07_16-04-01\n",
|
||
"2025-12-07 16:04:06,097\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_ea8a2f7a_2_text_det_box_thresh=0.6712,text_det_thresh=0.3932,text_det_unclip_ratio=0.0000,text_rec_score_thre_2025-12-07_16-04-01\n",
|
||
"\u001b[36m(trainable_paddle_ocr pid=19452)\u001b[0m [2025-12-07 16:04:31,654 E 19452 19604] core_worker_process.cc:837: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14\n",
|
||
"\u001b[36m(trainable_paddle_ocr pid=7472)\u001b[0m [2025-12-07 16:04:37,442 E 7472 7092] core_worker_process.cc:837: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14\n"
|
||
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|
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|
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|
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|
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|
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|
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|
||
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|
||
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|
||
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|
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|
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|
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|
||
"<tr><td>trainable_paddle_ocr_111e5a9e</td><td style=\"text-align: right;\">0.38641 </td><td style=\"text-align: right;\"> 5</td><td style=\"text-align: right;\">320.966</td><td style=\"text-align: right;\"> 64.0952</td><td style=\"text-align: right;\">0.455836 </td></tr>\n",
|
||
"<tr><td>trainable_paddle_ocr_1243723e</td><td style=\"text-align: right;\">0.0118532</td><td style=\"text-align: right;\"> 5</td><td style=\"text-align: right;\">355.679</td><td style=\"text-align: right;\"> 71.0243</td><td style=\"text-align: right;\">0.100441 </td></tr>\n",
|
||
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|
||
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|
||
"<tr><td>trainable_paddle_ocr_18966a33</td><td style=\"text-align: right;\">0.0116449</td><td style=\"text-align: right;\"> 5</td><td style=\"text-align: right;\">344.261</td><td style=\"text-align: right;\"> 68.7594</td><td style=\"text-align: right;\">0.0996499</td></tr>\n",
|
||
"<tr><td>trainable_paddle_ocr_2256e752</td><td style=\"text-align: right;\">0.0442921</td><td style=\"text-align: right;\"> 5</td><td style=\"text-align: right;\">349.417</td><td style=\"text-align: right;\"> 69.7759</td><td style=\"text-align: right;\">0.132838 </td></tr>\n",
|
||
"<tr><td>trainable_paddle_ocr_2533f368</td><td style=\"text-align: right;\">0.0720912</td><td style=\"text-align: right;\"> 5</td><td style=\"text-align: right;\">351.967</td><td style=\"text-align: right;\"> 70.2954</td><td style=\"text-align: right;\">0.179189 </td></tr>\n",
|
||
"<tr><td>trainable_paddle_ocr_2c691aaa</td><td style=\"text-align: right;\">0.0134006</td><td style=\"text-align: right;\"> 5</td><td style=\"text-align: right;\">347.145</td><td style=\"text-align: right;\"> 69.3242</td><td style=\"text-align: right;\">0.107419 </td></tr>\n",
|
||
"<tr><td>trainable_paddle_ocr_2e978bfa</td><td style=\"text-align: right;\">0.0134006</td><td style=\"text-align: right;\"> 5</td><td style=\"text-align: right;\">359.597</td><td style=\"text-align: right;\"> 71.8043</td><td style=\"text-align: right;\">0.107419 </td></tr>\n",
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"<tr><td>trainable_paddle_ocr_31e60691</td><td style=\"text-align: right;\">0.0130404</td><td style=\"text-align: right;\"> 5</td><td style=\"text-align: right;\">347.22 </td><td style=\"text-align: right;\"> 69.3455</td><td style=\"text-align: right;\">0.104854 </td></tr>\n",
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"<tr><td>trainable_paddle_ocr_3256ae36</td><td style=\"text-align: right;\">0.0124076</td><td style=\"text-align: right;\"> 5</td><td style=\"text-align: right;\">346.52 </td><td style=\"text-align: right;\"> 69.1998</td><td style=\"text-align: right;\">0.102016 </td></tr>\n",
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"<tr><td>trainable_paddle_ocr_33bdf2a9</td><td style=\"text-align: right;\">0.0709203</td><td style=\"text-align: right;\"> 5</td><td style=\"text-align: right;\">368.571</td><td style=\"text-align: right;\"> 73.625 </td><td style=\"text-align: right;\">0.17391 </td></tr>\n",
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"<tr><td>trainable_paddle_ocr_33ea1cc6</td><td style=\"text-align: right;\">0.0118754</td><td style=\"text-align: right;\"> 5</td><td style=\"text-align: right;\">355.524</td><td style=\"text-align: right;\"> 71.0081</td><td style=\"text-align: right;\">0.100476 </td></tr>\n",
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"<tr><td>trainable_paddle_ocr_415d7ba1</td><td style=\"text-align: right;\">0.516069 </td><td style=\"text-align: right;\"> 5</td><td style=\"text-align: right;\">326.657</td><td style=\"text-align: right;\"> 65.2351</td><td style=\"text-align: right;\">0.59453 </td></tr>\n",
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"<tr><td>trainable_paddle_ocr_442a2439</td><td style=\"text-align: right;\">0.0117601</td><td style=\"text-align: right;\"> 5</td><td style=\"text-align: right;\">345.943</td><td style=\"text-align: right;\"> 69.0839</td><td style=\"text-align: right;\">0.0996499</td></tr>\n",
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"<tr><td>trainable_paddle_ocr_451d018d</td><td style=\"text-align: right;\">0.0647995</td><td style=\"text-align: right;\"> 5</td><td style=\"text-align: right;\">357.17 </td><td style=\"text-align: right;\"> 71.3372</td><td style=\"text-align: right;\">0.164937 </td></tr>\n",
|
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"<tr><td>trainable_paddle_ocr_463fe5e7</td><td style=\"text-align: right;\">0.0123145</td><td style=\"text-align: right;\"> 5</td><td style=\"text-align: right;\">349.509</td><td style=\"text-align: right;\"> 69.8077</td><td style=\"text-align: right;\">0.102051 </td></tr>\n",
|
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"<tr><td>trainable_paddle_ocr_4762fbbb</td><td style=\"text-align: right;\">0.0135372</td><td style=\"text-align: right;\"> 5</td><td style=\"text-align: right;\">344.601</td><td style=\"text-align: right;\"> 68.8145</td><td style=\"text-align: right;\">0.108525 </td></tr>\n",
|
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"<tr><td>trainable_paddle_ocr_495075f5</td><td style=\"text-align: right;\">0.0122923</td><td style=\"text-align: right;\"> 5</td><td style=\"text-align: right;\">346.425</td><td style=\"text-align: right;\"> 69.1919</td><td style=\"text-align: right;\">0.102016 </td></tr>\n",
|
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"<tr><td>trainable_paddle_ocr_4a42a3ea</td><td style=\"text-align: right;\">0.0118754</td><td style=\"text-align: right;\"> 5</td><td style=\"text-align: right;\">350.244</td><td style=\"text-align: right;\"> 69.9484</td><td style=\"text-align: right;\">0.100476 </td></tr>\n",
|
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"<tr><td>trainable_paddle_ocr_51fb5915</td><td style=\"text-align: right;\">0.0115351</td><td style=\"text-align: right;\"> 5</td><td style=\"text-align: right;\">343.604</td><td style=\"text-align: right;\"> 68.6293</td><td style=\"text-align: right;\">0.0989016</td></tr>\n",
|
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"<tr><td>trainable_paddle_ocr_522ac97c</td><td style=\"text-align: right;\">0.0117638</td><td style=\"text-align: right;\"> 5</td><td style=\"text-align: right;\">344.038</td><td style=\"text-align: right;\"> 68.7183</td><td style=\"text-align: right;\">0.099689 </td></tr>\n",
|
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"<tr><td>trainable_paddle_ocr_54c45552</td><td style=\"text-align: right;\">0.0119742</td><td style=\"text-align: right;\"> 5</td><td style=\"text-align: right;\">346.941</td><td style=\"text-align: right;\"> 69.2981</td><td style=\"text-align: right;\">0.100476 </td></tr>\n",
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"<tr><td>trainable_paddle_ocr_5784f433</td><td style=\"text-align: right;\">0.0116503</td><td style=\"text-align: right;\"> 5</td><td style=\"text-align: right;\">343.513</td><td style=\"text-align: right;\"> 68.6003</td><td style=\"text-align: right;\">0.0989016</td></tr>\n",
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"<tr><td>trainable_paddle_ocr_5a62d5b6</td><td style=\"text-align: right;\">0.0662709</td><td style=\"text-align: right;\"> 5</td><td style=\"text-align: right;\">359.467</td><td style=\"text-align: right;\"> 71.7971</td><td style=\"text-align: right;\">0.168515 </td></tr>\n",
|
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"<tr><td>trainable_paddle_ocr_6b2e9b93</td><td style=\"text-align: right;\">0.0119742</td><td style=\"text-align: right;\"> 5</td><td style=\"text-align: right;\">346.414</td><td style=\"text-align: right;\"> 69.1859</td><td style=\"text-align: right;\">0.100476 </td></tr>\n",
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"<tr><td>trainable_paddle_ocr_70862adc</td><td style=\"text-align: right;\">0.0119685</td><td style=\"text-align: right;\"> 5</td><td style=\"text-align: right;\">345.403</td><td style=\"text-align: right;\"> 68.9856</td><td style=\"text-align: right;\">0.100441 </td></tr>\n",
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"<tr><td>trainable_paddle_ocr_7645b77c</td><td style=\"text-align: right;\">0.0125829</td><td style=\"text-align: right;\"> 5</td><td style=\"text-align: right;\">346.698</td><td style=\"text-align: right;\"> 69.2407</td><td style=\"text-align: right;\">0.10328 </td></tr>\n",
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"<tr><td>trainable_paddle_ocr_80ea65f2</td><td style=\"text-align: right;\">0.0645948</td><td style=\"text-align: right;\"> 5</td><td style=\"text-align: right;\">366.607</td><td style=\"text-align: right;\"> 73.222 </td><td style=\"text-align: right;\">0.164937 </td></tr>\n",
|
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"<tr><td>trainable_paddle_ocr_83af0528</td><td style=\"text-align: right;\">0.0116503</td><td style=\"text-align: right;\"> 5</td><td style=\"text-align: right;\">343.81 </td><td style=\"text-align: right;\"> 68.6691</td><td style=\"text-align: right;\">0.0989016</td></tr>\n",
|
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"<tr><td>trainable_paddle_ocr_8518cc40</td><td style=\"text-align: right;\">0.0134006</td><td style=\"text-align: right;\"> 5</td><td style=\"text-align: right;\">347.929</td><td style=\"text-align: right;\"> 69.49 </td><td style=\"text-align: right;\">0.107419 </td></tr>\n",
|
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"<tr><td>trainable_paddle_ocr_88bbe87d</td><td style=\"text-align: right;\">0.0120839</td><td style=\"text-align: right;\"> 5</td><td style=\"text-align: right;\">350.144</td><td style=\"text-align: right;\"> 69.9281</td><td style=\"text-align: right;\">0.101225 </td></tr>\n",
|
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"<tr><td>trainable_paddle_ocr_8abb3f37</td><td style=\"text-align: right;\">0.0123145</td><td style=\"text-align: right;\"> 5</td><td style=\"text-align: right;\">343.539</td><td style=\"text-align: right;\"> 68.6134</td><td style=\"text-align: right;\">0.102051 </td></tr>\n",
|
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"<tr><td>trainable_paddle_ocr_8b680875</td><td style=\"text-align: right;\">0.0121992</td><td style=\"text-align: right;\"> 5</td><td style=\"text-align: right;\">347.056</td><td style=\"text-align: right;\"> 69.3187</td><td style=\"text-align: right;\">0.101225 </td></tr>\n",
|
||
"<tr><td>trainable_paddle_ocr_8c12c55f</td><td style=\"text-align: right;\">0.0121862</td><td style=\"text-align: right;\"> 5</td><td style=\"text-align: right;\">343.939</td><td style=\"text-align: right;\"> 68.6927</td><td style=\"text-align: right;\">0.101228 </td></tr>\n",
|
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"<tr><td>trainable_paddle_ocr_92c48d07</td><td style=\"text-align: right;\">0.0119685</td><td style=\"text-align: right;\"> 5</td><td style=\"text-align: right;\">345.979</td><td style=\"text-align: right;\"> 69.0932</td><td style=\"text-align: right;\">0.100441 </td></tr>\n",
|
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"<tr><td>trainable_paddle_ocr_9a9b91e7</td><td style=\"text-align: right;\">0.044199 </td><td style=\"text-align: right;\"> 5</td><td style=\"text-align: right;\">343.539</td><td style=\"text-align: right;\"> 68.6156</td><td style=\"text-align: right;\">0.132047 </td></tr>\n",
|
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"<tr><td>trainable_paddle_ocr_9d90711d</td><td style=\"text-align: right;\">0.0115351</td><td style=\"text-align: right;\"> 5</td><td style=\"text-align: right;\">344.718</td><td style=\"text-align: right;\"> 68.8583</td><td style=\"text-align: right;\">0.0989016</td></tr>\n",
|
||
"<tr><td>trainable_paddle_ocr_a3a87765</td><td style=\"text-align: right;\">0.0117638</td><td style=\"text-align: right;\"> 5</td><td style=\"text-align: right;\">348.58 </td><td style=\"text-align: right;\"> 69.6186</td><td style=\"text-align: right;\">0.099689 </td></tr>\n",
|
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"<tr><td>trainable_paddle_ocr_a58d8109</td><td style=\"text-align: right;\">0.502513 </td><td style=\"text-align: right;\"> 5</td><td style=\"text-align: right;\">326.916</td><td style=\"text-align: right;\"> 65.2834</td><td style=\"text-align: right;\">0.567716 </td></tr>\n",
|
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"<tr><td>trainable_paddle_ocr_aa89fe7a</td><td style=\"text-align: right;\">0.0119907</td><td style=\"text-align: right;\"> 5</td><td style=\"text-align: right;\">346.54 </td><td style=\"text-align: right;\"> 69.2183</td><td style=\"text-align: right;\">0.100476 </td></tr>\n",
|
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"<tr><td>trainable_paddle_ocr_b0dda58b</td><td style=\"text-align: right;\">0.0124076</td><td style=\"text-align: right;\"> 5</td><td style=\"text-align: right;\">344.029</td><td style=\"text-align: right;\"> 68.7135</td><td style=\"text-align: right;\">0.102016 </td></tr>\n",
|
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"<tr><td>trainable_paddle_ocr_b3775034</td><td style=\"text-align: right;\">0.418109 </td><td style=\"text-align: right;\"> 5</td><td style=\"text-align: right;\">336.661</td><td style=\"text-align: right;\"> 67.2269</td><td style=\"text-align: right;\">0.50371 </td></tr>\n",
|
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"<tr><td>trainable_paddle_ocr_b67080f9</td><td style=\"text-align: right;\">0.0123145</td><td style=\"text-align: right;\"> 5</td><td style=\"text-align: right;\">345.495</td><td style=\"text-align: right;\"> 69.0121</td><td style=\"text-align: right;\">0.102051 </td></tr>\n",
|
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"<tr><td>trainable_paddle_ocr_bb4495b7</td><td style=\"text-align: right;\">0.0707008</td><td style=\"text-align: right;\"> 5</td><td style=\"text-align: right;\">356.322</td><td style=\"text-align: right;\"> 71.1644</td><td style=\"text-align: right;\">0.17391 </td></tr>\n",
|
||
"<tr><td>trainable_paddle_ocr_bf10d370</td><td style=\"text-align: right;\">0.197252 </td><td style=\"text-align: right;\"> 5</td><td style=\"text-align: right;\">350.147</td><td style=\"text-align: right;\"> 69.9364</td><td style=\"text-align: right;\">0.295353 </td></tr>\n",
|
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"<tr><td>trainable_paddle_ocr_c32d0d5e</td><td style=\"text-align: right;\">0.0133855</td><td style=\"text-align: right;\"> 5</td><td style=\"text-align: right;\">343.855</td><td style=\"text-align: right;\"> 68.6756</td><td style=\"text-align: right;\">0.109273 </td></tr>\n",
|
||
"<tr><td>trainable_paddle_ocr_ccb3f19a</td><td style=\"text-align: right;\">0.0119903</td><td style=\"text-align: right;\"> 5</td><td style=\"text-align: right;\">344.408</td><td style=\"text-align: right;\"> 68.7897</td><td style=\"text-align: right;\">0.0991043</td></tr>\n",
|
||
"<tr><td>trainable_paddle_ocr_cf2bad0c</td><td style=\"text-align: right;\">0.044199 </td><td style=\"text-align: right;\"> 5</td><td style=\"text-align: right;\">347.034</td><td style=\"text-align: right;\"> 69.311 </td><td style=\"text-align: right;\">0.132047 </td></tr>\n",
|
||
"<tr><td>trainable_paddle_ocr_d4d288c6</td><td style=\"text-align: right;\">0.0125829</td><td style=\"text-align: right;\"> 5</td><td style=\"text-align: right;\">349.232</td><td style=\"text-align: right;\"> 69.7463</td><td style=\"text-align: right;\">0.10328 </td></tr>\n",
|
||
"<tr><td>trainable_paddle_ocr_d5238c33</td><td style=\"text-align: right;\">0.0135159</td><td style=\"text-align: right;\"> 5</td><td style=\"text-align: right;\">353.851</td><td style=\"text-align: right;\"> 70.6623</td><td style=\"text-align: right;\">0.105003 </td></tr>\n",
|
||
"<tr><td>trainable_paddle_ocr_d9df79f3</td><td style=\"text-align: right;\">0.116825 </td><td style=\"text-align: right;\"> 5</td><td style=\"text-align: right;\">364.623</td><td style=\"text-align: right;\"> 72.8248</td><td style=\"text-align: right;\">0.22213 </td></tr>\n",
|
||
"<tr><td>trainable_paddle_ocr_daaec3f8</td><td style=\"text-align: right;\">0.0115351</td><td style=\"text-align: right;\"> 5</td><td style=\"text-align: right;\">343.697</td><td style=\"text-align: right;\"> 68.6424</td><td style=\"text-align: right;\">0.0989016</td></tr>\n",
|
||
"<tr><td>trainable_paddle_ocr_e326d901</td><td style=\"text-align: right;\">0.0121992</td><td style=\"text-align: right;\"> 5</td><td style=\"text-align: right;\">345.762</td><td style=\"text-align: right;\"> 69.0578</td><td style=\"text-align: right;\">0.101225 </td></tr>\n",
|
||
"<tr><td>trainable_paddle_ocr_e6821f34</td><td style=\"text-align: right;\">0.0124076</td><td style=\"text-align: right;\"> 5</td><td style=\"text-align: right;\">345.881</td><td style=\"text-align: right;\"> 69.0774</td><td style=\"text-align: right;\">0.102016 </td></tr>\n",
|
||
"<tr><td>trainable_paddle_ocr_e9a6b81f</td><td style=\"text-align: right;\">0.0115351</td><td style=\"text-align: right;\"> 5</td><td style=\"text-align: right;\">346.259</td><td style=\"text-align: right;\"> 69.1552</td><td style=\"text-align: right;\">0.0989016</td></tr>\n",
|
||
"<tr><td>trainable_paddle_ocr_e9d40333</td><td style=\"text-align: right;\">0.0124298</td><td style=\"text-align: right;\"> 5</td><td style=\"text-align: right;\">346.118</td><td style=\"text-align: right;\"> 69.1253</td><td style=\"text-align: right;\">0.102051 </td></tr>\n",
|
||
"<tr><td>trainable_paddle_ocr_ea8a2f7a</td><td style=\"text-align: right;\">0.039052 </td><td style=\"text-align: right;\"> 5</td><td style=\"text-align: right;\">354.615</td><td style=\"text-align: right;\"> 70.8221</td><td style=\"text-align: right;\">0.132086 </td></tr>\n",
|
||
"<tr><td>trainable_paddle_ocr_ebb12e5b</td><td style=\"text-align: right;\">0.0660624</td><td style=\"text-align: right;\"> 5</td><td style=\"text-align: right;\">359.097</td><td style=\"text-align: right;\"> 71.7257</td><td style=\"text-align: right;\">0.166192 </td></tr>\n",
|
||
"<tr><td>trainable_paddle_ocr_f2cb682e</td><td style=\"text-align: right;\">0.0123145</td><td style=\"text-align: right;\"> 5</td><td style=\"text-align: right;\">345.592</td><td style=\"text-align: right;\"> 69.0238</td><td style=\"text-align: right;\">0.102051 </td></tr>\n",
|
||
"<tr><td>trainable_paddle_ocr_fc54867b</td><td style=\"text-align: right;\">0.0124298</td><td style=\"text-align: right;\"> 5</td><td style=\"text-align: right;\">349.607</td><td style=\"text-align: right;\"> 69.8253</td><td style=\"text-align: right;\">0.102051 </td></tr>\n",
|
||
"</tbody>\n",
|
||
"</table>\n",
|
||
"</div>\n",
|
||
"<style>\n",
|
||
".trialProgress {\n",
|
||
" display: flex;\n",
|
||
" flex-direction: column;\n",
|
||
" color: var(--jp-ui-font-color1);\n",
|
||
"}\n",
|
||
".trialProgress h3 {\n",
|
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" font-weight: bold;\n",
|
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"}\n",
|
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".trialProgress td {\n",
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|
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{
|
||
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|
||
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|
||
"text": [
|
||
"2025-12-07 16:10:15,969\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_d5238c33_1_text_det_box_thresh=0.6230,text_det_thresh=0.0888,text_det_unclip_ratio=0.0000,text_rec_score_thre_2025-12-07_16-03-56\n",
|
||
"2025-12-07 16:10:16,056\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_ebb12e5b_3_text_det_box_thresh=0.2357,text_det_thresh=0.4329,text_det_unclip_ratio=0.0000,text_rec_score_thre_2025-12-07_16-10-16\n",
|
||
"2025-12-07 16:10:16,063\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_ebb12e5b_3_text_det_box_thresh=0.2357,text_det_thresh=0.4329,text_det_unclip_ratio=0.0000,text_rec_score_thre_2025-12-07_16-10-16\n",
|
||
"2025-12-07 16:10:20,414\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_ea8a2f7a_2_text_det_box_thresh=0.6712,text_det_thresh=0.3932,text_det_unclip_ratio=0.0000,text_rec_score_thre_2025-12-07_16-04-01\n",
|
||
"2025-12-07 16:10:22,097\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_ebb12e5b_3_text_det_box_thresh=0.2357,text_det_thresh=0.4329,text_det_unclip_ratio=0.0000,text_rec_score_thre_2025-12-07_16-10-16\n",
|
||
"2025-12-07 16:10:22,097\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_ebb12e5b_3_text_det_box_thresh=0.2357,text_det_thresh=0.4329,text_det_unclip_ratio=0.0000,text_rec_score_thre_2025-12-07_16-10-16\n",
|
||
"2025-12-07 16:10:22,097\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_b3775034_4_text_det_box_thresh=0.3377,text_det_thresh=0.0641,text_det_unclip_ratio=0.0000,text_rec_score_thre_2025-12-07_16-10-22\n",
|
||
"2025-12-07 16:10:22,097\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_b3775034_4_text_det_box_thresh=0.3377,text_det_thresh=0.0641,text_det_unclip_ratio=0.0000,text_rec_score_thre_2025-12-07_16-10-22\n",
|
||
"2025-12-07 16:10:26,662\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_b3775034_4_text_det_box_thresh=0.3377,text_det_thresh=0.0641,text_det_unclip_ratio=0.0000,text_rec_score_thre_2025-12-07_16-10-22\n",
|
||
"2025-12-07 16:10:26,664\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_b3775034_4_text_det_box_thresh=0.3377,text_det_thresh=0.0641,text_det_unclip_ratio=0.0000,text_rec_score_thre_2025-12-07_16-10-22\n",
|
||
"\u001b[36m(trainable_paddle_ocr pid=21480)\u001b[0m [2025-12-07 16:10:51,593 E 21480 13444] core_worker_process.cc:837: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14\n",
|
||
"\u001b[36m(trainable_paddle_ocr pid=23084)\u001b[0m [2025-12-07 16:10:56,943 E 23084 15580] core_worker_process.cc:837: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14\n",
|
||
"2025-12-07 16:16:23,218\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_b3775034_4_text_det_box_thresh=0.3377,text_det_thresh=0.0641,text_det_unclip_ratio=0.0000,text_rec_score_thre_2025-12-07_16-10-22\n",
|
||
"2025-12-07 16:16:23,261\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_bf10d370_5_text_det_box_thresh=0.6902,text_det_thresh=0.6720,text_det_unclip_ratio=0.0000,text_rec_score_thre_2025-12-07_16-16-23\n",
|
||
"2025-12-07 16:16:23,263\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_bf10d370_5_text_det_box_thresh=0.6902,text_det_thresh=0.6720,text_det_unclip_ratio=0.0000,text_rec_score_thre_2025-12-07_16-16-23\n",
|
||
"2025-12-07 16:16:28,918\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_bf10d370_5_text_det_box_thresh=0.6902,text_det_thresh=0.6720,text_det_unclip_ratio=0.0000,text_rec_score_thre_2025-12-07_16-16-23\n",
|
||
"2025-12-07 16:16:28,918\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_bf10d370_5_text_det_box_thresh=0.6902,text_det_thresh=0.6720,text_det_unclip_ratio=0.0000,text_rec_score_thre_2025-12-07_16-16-23\n",
|
||
"2025-12-07 16:16:41,652\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_ebb12e5b_3_text_det_box_thresh=0.2357,text_det_thresh=0.4329,text_det_unclip_ratio=0.0000,text_rec_score_thre_2025-12-07_16-10-16\n",
|
||
"2025-12-07 16:16:41,663\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_111e5a9e_6_text_det_box_thresh=0.4833,text_det_thresh=0.0448,text_det_unclip_ratio=0.0000,text_rec_score_thre_2025-12-07_16-16-41\n",
|
||
"2025-12-07 16:16:41,665\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_111e5a9e_6_text_det_box_thresh=0.4833,text_det_thresh=0.0448,text_det_unclip_ratio=0.0000,text_rec_score_thre_2025-12-07_16-16-41\n",
|
||
"2025-12-07 16:16:46,207\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_111e5a9e_6_text_det_box_thresh=0.4833,text_det_thresh=0.0448,text_det_unclip_ratio=0.0000,text_rec_score_thre_2025-12-07_16-16-41\n",
|
||
"2025-12-07 16:16:46,207\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_111e5a9e_6_text_det_box_thresh=0.4833,text_det_thresh=0.0448,text_det_unclip_ratio=0.0000,text_rec_score_thre_2025-12-07_16-16-41\n",
|
||
"\u001b[36m(trainable_paddle_ocr pid=26140)\u001b[0m [2025-12-07 16:16:58,481 E 26140 16220] core_worker_process.cc:837: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14\n",
|
||
"\u001b[36m(trainable_paddle_ocr pid=20664)\u001b[0m [2025-12-07 16:17:16,506 E 20664 20720] core_worker_process.cc:837: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14\n",
|
||
"2025-12-07 16:22:27,297\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_111e5a9e_6_text_det_box_thresh=0.4833,text_det_thresh=0.0448,text_det_unclip_ratio=0.0000,text_rec_score_thre_2025-12-07_16-16-41\n",
|
||
"2025-12-07 16:22:27,312\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_415d7ba1_7_text_det_box_thresh=0.5234,text_det_thresh=0.0170,text_det_unclip_ratio=0.0000,text_rec_score_thre_2025-12-07_16-22-27\n",
|
||
"2025-12-07 16:22:27,316\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_415d7ba1_7_text_det_box_thresh=0.5234,text_det_thresh=0.0170,text_det_unclip_ratio=0.0000,text_rec_score_thre_2025-12-07_16-22-27\n",
|
||
"2025-12-07 16:22:32,726\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_415d7ba1_7_text_det_box_thresh=0.5234,text_det_thresh=0.0170,text_det_unclip_ratio=0.0000,text_rec_score_thre_2025-12-07_16-22-27\n",
|
||
"2025-12-07 16:22:32,728\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_415d7ba1_7_text_det_box_thresh=0.5234,text_det_thresh=0.0170,text_det_unclip_ratio=0.0000,text_rec_score_thre_2025-12-07_16-22-27\n",
|
||
"2025-12-07 16:22:39,838\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_bf10d370_5_text_det_box_thresh=0.6902,text_det_thresh=0.6720,text_det_unclip_ratio=0.0000,text_rec_score_thre_2025-12-07_16-16-23\n",
|
||
"2025-12-07 16:22:39,854\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_a58d8109_8_text_det_box_thresh=0.6706,text_det_thresh=0.0402,text_det_unclip_ratio=0.0000,text_rec_score_thre_2025-12-07_16-22-39\n",
|
||
"2025-12-07 16:22:39,854\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_a58d8109_8_text_det_box_thresh=0.6706,text_det_thresh=0.0402,text_det_unclip_ratio=0.0000,text_rec_score_thre_2025-12-07_16-22-39\n",
|
||
"2025-12-07 16:22:44,482\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_a58d8109_8_text_det_box_thresh=0.6706,text_det_thresh=0.0402,text_det_unclip_ratio=0.0000,text_rec_score_thre_2025-12-07_16-22-39\n",
|
||
"2025-12-07 16:22:44,484\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_a58d8109_8_text_det_box_thresh=0.6706,text_det_thresh=0.0402,text_det_unclip_ratio=0.0000,text_rec_score_thre_2025-12-07_16-22-39\n",
|
||
"\u001b[36m(trainable_paddle_ocr pid=23848)\u001b[0m [2025-12-07 16:23:02,571 E 23848 12908] core_worker_process.cc:837: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14\n",
|
||
"\u001b[36m(trainable_paddle_ocr pid=25248)\u001b[0m [2025-12-07 16:23:14,789 E 25248 4036] core_worker_process.cc:837: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14\n",
|
||
"2025-12-07 16:28:20,034\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_415d7ba1_7_text_det_box_thresh=0.5234,text_det_thresh=0.0170,text_det_unclip_ratio=0.0000,text_rec_score_thre_2025-12-07_16-22-27\n",
|
||
"2025-12-07 16:28:20,052\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_33bdf2a9_9_text_det_box_thresh=0.4900,text_det_thresh=0.4347,text_det_unclip_ratio=0.0000,text_rec_score_thre_2025-12-07_16-28-20\n",
|
||
"2025-12-07 16:28:20,055\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_33bdf2a9_9_text_det_box_thresh=0.4900,text_det_thresh=0.4347,text_det_unclip_ratio=0.0000,text_rec_score_thre_2025-12-07_16-28-20\n",
|
||
"2025-12-07 16:28:24,790\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_33bdf2a9_9_text_det_box_thresh=0.4900,text_det_thresh=0.4347,text_det_unclip_ratio=0.0000,text_rec_score_thre_2025-12-07_16-28-20\n",
|
||
"2025-12-07 16:28:24,790\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_33bdf2a9_9_text_det_box_thresh=0.4900,text_det_thresh=0.4347,text_det_unclip_ratio=0.0000,text_rec_score_thre_2025-12-07_16-28-20\n",
|
||
"2025-12-07 16:28:30,585\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_a58d8109_8_text_det_box_thresh=0.6706,text_det_thresh=0.0402,text_det_unclip_ratio=0.0000,text_rec_score_thre_2025-12-07_16-22-39\n",
|
||
"2025-12-07 16:28:30,605\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_d9df79f3_10_text_det_box_thresh=0.6262,text_det_thresh=0.1781,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_16-28-30\n",
|
||
"2025-12-07 16:28:30,607\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_d9df79f3_10_text_det_box_thresh=0.6262,text_det_thresh=0.1781,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_16-28-30\n",
|
||
"2025-12-07 16:28:35,143\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_d9df79f3_10_text_det_box_thresh=0.6262,text_det_thresh=0.1781,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_16-28-30\n",
|
||
"2025-12-07 16:28:35,143\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_d9df79f3_10_text_det_box_thresh=0.6262,text_det_thresh=0.1781,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_16-28-30\n",
|
||
"\u001b[36m(trainable_paddle_ocr pid=24024)\u001b[0m [2025-12-07 16:28:54,997 E 24024 23472] core_worker_process.cc:837: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14\n",
|
||
"\u001b[36m(trainable_paddle_ocr pid=5368)\u001b[0m [2025-12-07 16:29:05,433 E 5368 24544] core_worker_process.cc:837: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14\n",
|
||
"2025-12-07 16:34:52,986\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_33bdf2a9_9_text_det_box_thresh=0.4900,text_det_thresh=0.4347,text_det_unclip_ratio=0.0000,text_rec_score_thre_2025-12-07_16-28-20\n",
|
||
"2025-12-07 16:34:53,020\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_80ea65f2_11_text_det_box_thresh=0.2514,text_det_thresh=0.6011,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_16-34-53\n",
|
||
"2025-12-07 16:34:53,024\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_80ea65f2_11_text_det_box_thresh=0.2514,text_det_thresh=0.6011,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_16-34-53\n",
|
||
"2025-12-07 16:34:58,668\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_80ea65f2_11_text_det_box_thresh=0.2514,text_det_thresh=0.6011,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_16-34-53\n",
|
||
"2025-12-07 16:34:58,670\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_80ea65f2_11_text_det_box_thresh=0.2514,text_det_thresh=0.6011,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_16-34-53\n",
|
||
"2025-12-07 16:34:59,856\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_d9df79f3_10_text_det_box_thresh=0.6262,text_det_thresh=0.1781,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_16-28-30\n",
|
||
"2025-12-07 16:34:59,928\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_2e978bfa_12_text_det_box_thresh=0.0777,text_det_thresh=0.2349,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_16-34-59\n",
|
||
"2025-12-07 16:34:59,933\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_2e978bfa_12_text_det_box_thresh=0.0777,text_det_thresh=0.2349,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_16-34-59\n",
|
||
"2025-12-07 16:35:04,574\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_2e978bfa_12_text_det_box_thresh=0.0777,text_det_thresh=0.2349,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_16-34-59\n",
|
||
"2025-12-07 16:35:04,576\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_2e978bfa_12_text_det_box_thresh=0.0777,text_det_thresh=0.2349,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_16-34-59\n",
|
||
"\u001b[36m(trainable_paddle_ocr pid=14064)\u001b[0m [2025-12-07 16:35:28,312 E 14064 18904] core_worker_process.cc:837: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14\n",
|
||
"\u001b[36m(trainable_paddle_ocr pid=11060)\u001b[0m [2025-12-07 16:35:34,907 E 11060 16108] core_worker_process.cc:837: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14\n",
|
||
"2025-12-07 16:41:24,926\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_2e978bfa_12_text_det_box_thresh=0.0777,text_det_thresh=0.2349,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_16-34-59\n",
|
||
"2025-12-07 16:41:24,993\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_8518cc40_13_text_det_box_thresh=0.0002,text_det_thresh=0.2226,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_16-41-24\n",
|
||
"2025-12-07 16:41:24,996\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_8518cc40_13_text_det_box_thresh=0.0002,text_det_thresh=0.2226,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_16-41-24\n",
|
||
"2025-12-07 16:41:26,379\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_80ea65f2_11_text_det_box_thresh=0.2514,text_det_thresh=0.6011,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_16-34-53\n",
|
||
"2025-12-07 16:41:30,746\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_8518cc40_13_text_det_box_thresh=0.0002,text_det_thresh=0.2226,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_16-41-24\n",
|
||
"2025-12-07 16:41:30,746\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_8518cc40_13_text_det_box_thresh=0.0002,text_det_thresh=0.2226,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_16-41-24\n",
|
||
"2025-12-07 16:41:30,767\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_2c691aaa_14_text_det_box_thresh=0.0303,text_det_thresh=0.2247,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_16-41-30\n",
|
||
"2025-12-07 16:41:30,770\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_2c691aaa_14_text_det_box_thresh=0.0303,text_det_thresh=0.2247,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_16-41-30\n",
|
||
"2025-12-07 16:41:35,236\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_2c691aaa_14_text_det_box_thresh=0.0303,text_det_thresh=0.2247,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_16-41-30\n",
|
||
"2025-12-07 16:41:35,236\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_2c691aaa_14_text_det_box_thresh=0.0303,text_det_thresh=0.2247,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_16-41-30\n",
|
||
"\u001b[36m(trainable_paddle_ocr pid=21016)\u001b[0m [2025-12-07 16:42:00,269 E 21016 19044] core_worker_process.cc:837: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14\n",
|
||
"\u001b[36m(trainable_paddle_ocr pid=21540)\u001b[0m [2025-12-07 16:42:06,593 E 21540 1744] core_worker_process.cc:837: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14\n",
|
||
"2025-12-07 16:47:39,341\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_8518cc40_13_text_det_box_thresh=0.0002,text_det_thresh=0.2226,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_16-41-24\n",
|
||
"2025-12-07 16:47:39,378\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_31e60691_15_text_det_box_thresh=0.0020,text_det_thresh=0.2591,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_16-47-39\n",
|
||
"2025-12-07 16:47:39,378\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_31e60691_15_text_det_box_thresh=0.0020,text_det_thresh=0.2591,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_16-47-39\n",
|
||
"2025-12-07 16:47:41,612\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_2c691aaa_14_text_det_box_thresh=0.0303,text_det_thresh=0.2247,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_16-41-30\n",
|
||
"2025-12-07 16:47:44,526\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_31e60691_15_text_det_box_thresh=0.0020,text_det_thresh=0.2591,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_16-47-39\n",
|
||
"2025-12-07 16:47:44,526\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_31e60691_15_text_det_box_thresh=0.0020,text_det_thresh=0.2591,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_16-47-39\n",
|
||
"2025-12-07 16:47:44,541\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_d4d288c6_16_text_det_box_thresh=0.0034,text_det_thresh=0.2734,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_16-47-44\n",
|
||
"2025-12-07 16:47:44,544\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_d4d288c6_16_text_det_box_thresh=0.0034,text_det_thresh=0.2734,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_16-47-44\n",
|
||
"2025-12-07 16:47:49,055\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_d4d288c6_16_text_det_box_thresh=0.0034,text_det_thresh=0.2734,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_16-47-44\n",
|
||
"2025-12-07 16:47:49,057\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_d4d288c6_16_text_det_box_thresh=0.0034,text_det_thresh=0.2734,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_16-47-44\n",
|
||
"\u001b[36m(trainable_paddle_ocr pid=17532)\u001b[0m [2025-12-07 16:48:14,498 E 17532 10276] core_worker_process.cc:837: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14\n",
|
||
"2025-12-07 16:53:52,583\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_31e60691_15_text_det_box_thresh=0.0020,text_det_thresh=0.2591,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_16-47-39\n",
|
||
"2025-12-07 16:53:52,603\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_7645b77c_17_text_det_box_thresh=0.1138,text_det_thresh=0.2792,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_16-53-52\n",
|
||
"2025-12-07 16:53:52,608\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_7645b77c_17_text_det_box_thresh=0.1138,text_det_thresh=0.2792,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_16-53-52\n",
|
||
"2025-12-07 16:53:57,961\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_d4d288c6_16_text_det_box_thresh=0.0034,text_det_thresh=0.2734,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_16-47-44\n",
|
||
"2025-12-07 16:53:57,971\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_7645b77c_17_text_det_box_thresh=0.1138,text_det_thresh=0.2792,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_16-53-52\n",
|
||
"2025-12-07 16:53:57,971\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_7645b77c_17_text_det_box_thresh=0.1138,text_det_thresh=0.2792,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_16-53-52\n",
|
||
"2025-12-07 16:53:57,993\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_3256ae36_18_text_det_box_thresh=0.1292,text_det_thresh=0.3099,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_16-53-57\n",
|
||
"2025-12-07 16:53:57,996\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_3256ae36_18_text_det_box_thresh=0.1292,text_det_thresh=0.3099,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_16-53-57\n",
|
||
"2025-12-07 16:54:02,522\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_3256ae36_18_text_det_box_thresh=0.1292,text_det_thresh=0.3099,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_16-53-57\n",
|
||
"2025-12-07 16:54:02,522\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_3256ae36_18_text_det_box_thresh=0.1292,text_det_thresh=0.3099,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_16-53-57\n",
|
||
"\u001b[36m(trainable_paddle_ocr pid=2272)\u001b[0m [2025-12-07 16:54:27,753 E 2272 2144] core_worker_process.cc:837: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14\u001b[32m [repeated 2x across cluster]\u001b[0m\n",
|
||
"\u001b[36m(trainable_paddle_ocr pid=6604)\u001b[0m [2025-12-07 16:54:32,853 E 6604 7428] core_worker_process.cc:837: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14\n",
|
||
"2025-12-07 17:00:05,436\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_7645b77c_17_text_det_box_thresh=0.1138,text_det_thresh=0.2792,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_16-53-52\n",
|
||
"2025-12-07 17:00:05,471\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_b0dda58b_19_text_det_box_thresh=0.1178,text_det_thresh=0.3150,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_17-00-05\n",
|
||
"2025-12-07 17:00:05,471\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_b0dda58b_19_text_det_box_thresh=0.1178,text_det_thresh=0.3150,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_17-00-05\n",
|
||
"2025-12-07 17:00:08,537\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_3256ae36_18_text_det_box_thresh=0.1292,text_det_thresh=0.3099,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_16-53-57\n",
|
||
"2025-12-07 17:00:11,016\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_b0dda58b_19_text_det_box_thresh=0.1178,text_det_thresh=0.3150,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_17-00-05\n",
|
||
"2025-12-07 17:00:11,017\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_b0dda58b_19_text_det_box_thresh=0.1178,text_det_thresh=0.3150,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_17-00-05\n",
|
||
"2025-12-07 17:00:11,026\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_e9d40333_20_text_det_box_thresh=0.1569,text_det_thresh=0.5303,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_17-00-11\n",
|
||
"2025-12-07 17:00:11,034\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_e9d40333_20_text_det_box_thresh=0.1569,text_det_thresh=0.5303,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_17-00-11\n",
|
||
"2025-12-07 17:00:15,508\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_e9d40333_20_text_det_box_thresh=0.1569,text_det_thresh=0.5303,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_17-00-11\n",
|
||
"2025-12-07 17:00:15,509\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_e9d40333_20_text_det_box_thresh=0.1569,text_det_thresh=0.5303,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_17-00-11\n",
|
||
"\u001b[36m(trainable_paddle_ocr pid=9732)\u001b[0m [2025-12-07 17:00:40,741 E 9732 14552] core_worker_process.cc:837: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14\n",
|
||
"\u001b[36m(trainable_paddle_ocr pid=23416)\u001b[0m [2025-12-07 17:00:45,836 E 23416 4196] core_worker_process.cc:837: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14\n",
|
||
"2025-12-07 17:06:15,896\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_b0dda58b_19_text_det_box_thresh=0.1178,text_det_thresh=0.3150,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_17-00-05\n",
|
||
"2025-12-07 17:06:15,950\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_aa89fe7a_21_text_det_box_thresh=0.1621,text_det_thresh=0.5040,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_17-06-15\n",
|
||
"2025-12-07 17:06:15,953\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_aa89fe7a_21_text_det_box_thresh=0.1621,text_det_thresh=0.5040,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_17-06-15\n",
|
||
"2025-12-07 17:06:21,172\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_e9d40333_20_text_det_box_thresh=0.1569,text_det_thresh=0.5303,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_17-00-11\n",
|
||
"2025-12-07 17:06:21,708\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_aa89fe7a_21_text_det_box_thresh=0.1621,text_det_thresh=0.5040,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_17-06-15\n",
|
||
"2025-12-07 17:06:21,709\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_aa89fe7a_21_text_det_box_thresh=0.1621,text_det_thresh=0.5040,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_17-06-15\n",
|
||
"2025-12-07 17:06:21,722\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_92c48d07_22_text_det_box_thresh=0.1864,text_det_thresh=0.3332,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_17-06-21\n",
|
||
"2025-12-07 17:06:21,724\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_92c48d07_22_text_det_box_thresh=0.1864,text_det_thresh=0.3332,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_17-06-21\n",
|
||
"2025-12-07 17:06:26,213\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_92c48d07_22_text_det_box_thresh=0.1864,text_det_thresh=0.3332,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_17-06-21\n",
|
||
"2025-12-07 17:06:26,213\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_92c48d07_22_text_det_box_thresh=0.1864,text_det_thresh=0.3332,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_17-06-21\n",
|
||
"\u001b[36m(trainable_paddle_ocr pid=16200)\u001b[0m [2025-12-07 17:06:51,279 E 16200 7620] core_worker_process.cc:837: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14\n",
|
||
"\u001b[36m(trainable_paddle_ocr pid=15432)\u001b[0m [2025-12-07 17:06:56,512 E 15432 12008] core_worker_process.cc:837: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14\n",
|
||
"2025-12-07 17:12:28,470\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_aa89fe7a_21_text_det_box_thresh=0.1621,text_det_thresh=0.5040,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_17-06-15\n",
|
||
"2025-12-07 17:12:28,508\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_187790d7_23_text_det_box_thresh=0.2353,text_det_thresh=0.3373,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_17-12-28\n",
|
||
"2025-12-07 17:12:28,513\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_187790d7_23_text_det_box_thresh=0.2353,text_det_thresh=0.3373,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_17-12-28\n",
|
||
"2025-12-07 17:12:31,317\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_92c48d07_22_text_det_box_thresh=0.1864,text_det_thresh=0.3332,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_17-06-21\n",
|
||
"2025-12-07 17:12:33,695\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_187790d7_23_text_det_box_thresh=0.2353,text_det_thresh=0.3373,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_17-12-28\n",
|
||
"2025-12-07 17:12:33,695\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_187790d7_23_text_det_box_thresh=0.2353,text_det_thresh=0.3373,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_17-12-28\n",
|
||
"2025-12-07 17:12:33,716\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_442a2439_24_text_det_box_thresh=0.2123,text_det_thresh=0.5098,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_17-12-33\n",
|
||
"2025-12-07 17:12:33,718\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_442a2439_24_text_det_box_thresh=0.2123,text_det_thresh=0.5098,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_17-12-33\n",
|
||
"2025-12-07 17:12:38,168\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_442a2439_24_text_det_box_thresh=0.2123,text_det_thresh=0.5098,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_17-12-33\n",
|
||
"2025-12-07 17:12:38,168\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_442a2439_24_text_det_box_thresh=0.2123,text_det_thresh=0.5098,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_17-12-33\n",
|
||
"\u001b[36m(trainable_paddle_ocr pid=24676)\u001b[0m [2025-12-07 17:13:03,575 E 24676 21816] core_worker_process.cc:837: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14\n",
|
||
"2025-12-07 17:18:38,200\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_187790d7_23_text_det_box_thresh=0.2353,text_det_thresh=0.3373,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_17-12-28\n",
|
||
"2025-12-07 17:18:38,251\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_70862adc_25_text_det_box_thresh=0.2163,text_det_thresh=0.3964,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_17-18-38\n",
|
||
"2025-12-07 17:18:38,254\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_70862adc_25_text_det_box_thresh=0.2163,text_det_thresh=0.3964,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_17-18-38\n",
|
||
"2025-12-07 17:18:42,934\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_442a2439_24_text_det_box_thresh=0.2123,text_det_thresh=0.5098,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_17-12-33\n",
|
||
"2025-12-07 17:18:43,890\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_70862adc_25_text_det_box_thresh=0.2163,text_det_thresh=0.3964,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_17-18-38\n",
|
||
"2025-12-07 17:18:43,892\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_70862adc_25_text_det_box_thresh=0.2163,text_det_thresh=0.3964,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_17-18-38\n",
|
||
"2025-12-07 17:18:43,903\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_e6821f34_26_text_det_box_thresh=0.2408,text_det_thresh=0.3669,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_17-18-43\n",
|
||
"2025-12-07 17:18:43,904\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_e6821f34_26_text_det_box_thresh=0.2408,text_det_thresh=0.3669,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_17-18-43\n",
|
||
"2025-12-07 17:18:48,373\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_e6821f34_26_text_det_box_thresh=0.2408,text_det_thresh=0.3669,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_17-18-43\n",
|
||
"2025-12-07 17:18:48,373\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_e6821f34_26_text_det_box_thresh=0.2408,text_det_thresh=0.3669,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_17-18-43\n",
|
||
"\u001b[36m(trainable_paddle_ocr pid=15412)\u001b[0m [2025-12-07 17:19:13,443 E 15412 9512] core_worker_process.cc:837: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14\u001b[32m [repeated 2x across cluster]\u001b[0m\n",
|
||
"\u001b[36m(trainable_paddle_ocr pid=26088)\u001b[0m [2025-12-07 17:19:18,671 E 26088 10400] core_worker_process.cc:837: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14\n",
|
||
"2025-12-07 17:24:49,882\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_70862adc_25_text_det_box_thresh=0.2163,text_det_thresh=0.3964,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_17-18-38\n",
|
||
"2025-12-07 17:24:49,909\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_8b680875_27_text_det_box_thresh=0.3193,text_det_thresh=0.5312,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_17-24-49\n",
|
||
"2025-12-07 17:24:49,911\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_8b680875_27_text_det_box_thresh=0.3193,text_det_thresh=0.5312,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_17-24-49\n",
|
||
"2025-12-07 17:24:53,650\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_e6821f34_26_text_det_box_thresh=0.2408,text_det_thresh=0.3669,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_17-18-43\n",
|
||
"2025-12-07 17:24:55,137\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_8b680875_27_text_det_box_thresh=0.3193,text_det_thresh=0.5312,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_17-24-49\n",
|
||
"2025-12-07 17:24:55,137\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_8b680875_27_text_det_box_thresh=0.3193,text_det_thresh=0.5312,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_17-24-49\n",
|
||
"2025-12-07 17:24:55,153\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_fc54867b_28_text_det_box_thresh=0.3043,text_det_thresh=0.5034,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_17-24-55\n",
|
||
"2025-12-07 17:24:55,156\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_fc54867b_28_text_det_box_thresh=0.3043,text_det_thresh=0.5034,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_17-24-55\n",
|
||
"2025-12-07 17:24:59,622\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_fc54867b_28_text_det_box_thresh=0.3043,text_det_thresh=0.5034,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_17-24-55\n",
|
||
"2025-12-07 17:24:59,622\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_fc54867b_28_text_det_box_thresh=0.3043,text_det_thresh=0.5034,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_17-24-55\n",
|
||
"\u001b[36m(trainable_paddle_ocr pid=1720)\u001b[0m [2025-12-07 17:25:25,047 E 1720 25468] core_worker_process.cc:837: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14\n",
|
||
"2025-12-07 17:31:02,389\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_8b680875_27_text_det_box_thresh=0.3193,text_det_thresh=0.5312,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_17-24-49\n",
|
||
"2025-12-07 17:31:02,469\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_c32d0d5e_29_text_det_box_thresh=0.3985,text_det_thresh=0.1530,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_17-31-02\n",
|
||
"2025-12-07 17:31:02,473\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_c32d0d5e_29_text_det_box_thresh=0.3985,text_det_thresh=0.1530,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_17-31-02\n",
|
||
"2025-12-07 17:31:08,377\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_fc54867b_28_text_det_box_thresh=0.3043,text_det_thresh=0.5034,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_17-24-55\n",
|
||
"2025-12-07 17:31:08,467\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_c32d0d5e_29_text_det_box_thresh=0.3985,text_det_thresh=0.1530,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_17-31-02\n",
|
||
"2025-12-07 17:31:08,467\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_c32d0d5e_29_text_det_box_thresh=0.3985,text_det_thresh=0.1530,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_17-31-02\n",
|
||
"2025-12-07 17:31:08,487\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_4762fbbb_30_text_det_box_thresh=0.4010,text_det_thresh=0.1334,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_17-31-08\n",
|
||
"2025-12-07 17:31:08,489\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_4762fbbb_30_text_det_box_thresh=0.4010,text_det_thresh=0.1334,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_17-31-08\n",
|
||
"2025-12-07 17:31:12,960\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_4762fbbb_30_text_det_box_thresh=0.4010,text_det_thresh=0.1334,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_17-31-08\n",
|
||
"2025-12-07 17:31:12,962\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_4762fbbb_30_text_det_box_thresh=0.4010,text_det_thresh=0.1334,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_17-31-08\n",
|
||
"\u001b[36m(trainable_paddle_ocr pid=25808)\u001b[0m [2025-12-07 17:31:37,810 E 25808 21612] core_worker_process.cc:837: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14\u001b[32m [repeated 2x across cluster]\u001b[0m\n",
|
||
"\u001b[36m(trainable_paddle_ocr pid=20760)\u001b[0m [2025-12-07 17:31:43,311 E 20760 9512] core_worker_process.cc:837: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14\n",
|
||
"2025-12-07 17:37:12,922\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_c32d0d5e_29_text_det_box_thresh=0.3985,text_det_thresh=0.1530,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_17-31-02\n",
|
||
"2025-12-07 17:37:12,971\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_522ac97c_31_text_det_box_thresh=0.4028,text_det_thresh=0.4490,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_17-37-12\n",
|
||
"2025-12-07 17:37:12,975\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_522ac97c_31_text_det_box_thresh=0.4028,text_det_thresh=0.4490,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_17-37-12\n",
|
||
"2025-12-07 17:37:16,310\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_4762fbbb_30_text_det_box_thresh=0.4010,text_det_thresh=0.1334,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_17-31-08\n",
|
||
"2025-12-07 17:37:18,530\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_522ac97c_31_text_det_box_thresh=0.4028,text_det_thresh=0.4490,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_17-37-12\n",
|
||
"2025-12-07 17:37:18,538\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_522ac97c_31_text_det_box_thresh=0.4028,text_det_thresh=0.4490,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_17-37-12\n",
|
||
"2025-12-07 17:37:18,551\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_5784f433_32_text_det_box_thresh=0.1928,text_det_thresh=0.4620,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_17-37-18\n",
|
||
"2025-12-07 17:37:18,553\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_5784f433_32_text_det_box_thresh=0.1928,text_det_thresh=0.4620,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_17-37-18\n",
|
||
"2025-12-07 17:37:23,024\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_5784f433_32_text_det_box_thresh=0.1928,text_det_thresh=0.4620,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_17-37-18\n",
|
||
"2025-12-07 17:37:23,030\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_5784f433_32_text_det_box_thresh=0.1928,text_det_thresh=0.4620,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_17-37-18\n",
|
||
"\u001b[36m(trainable_paddle_ocr pid=2372)\u001b[0m [2025-12-07 17:37:49,189 E 2372 11208] core_worker_process.cc:837: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14\n",
|
||
"2025-12-07 17:43:23,269\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_522ac97c_31_text_det_box_thresh=0.4028,text_det_thresh=0.4490,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_17-37-12\n",
|
||
"2025-12-07 17:43:23,297\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_83af0528_33_text_det_box_thresh=0.1846,text_det_thresh=0.4663,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_17-43-23\n",
|
||
"2025-12-07 17:43:23,299\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_83af0528_33_text_det_box_thresh=0.1846,text_det_thresh=0.4663,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_17-43-23\n",
|
||
"2025-12-07 17:43:25,962\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_5784f433_32_text_det_box_thresh=0.1928,text_det_thresh=0.4620,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_17-37-18\n",
|
||
"2025-12-07 17:43:28,377\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_83af0528_33_text_det_box_thresh=0.1846,text_det_thresh=0.4663,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_17-43-23\n",
|
||
"2025-12-07 17:43:28,377\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_83af0528_33_text_det_box_thresh=0.1846,text_det_thresh=0.4663,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_17-43-23\n",
|
||
"2025-12-07 17:43:28,392\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_12cbaa22_34_text_det_box_thresh=0.4056,text_det_thresh=0.4728,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_17-43-28\n",
|
||
"2025-12-07 17:43:28,394\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_12cbaa22_34_text_det_box_thresh=0.4056,text_det_thresh=0.4728,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_17-43-28\n",
|
||
"2025-12-07 17:43:32,822\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_12cbaa22_34_text_det_box_thresh=0.4056,text_det_thresh=0.4728,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_17-43-28\n",
|
||
"2025-12-07 17:43:32,822\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_12cbaa22_34_text_det_box_thresh=0.4056,text_det_thresh=0.4728,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_17-43-28\n",
|
||
"\u001b[36m(trainable_paddle_ocr pid=9832)\u001b[0m [2025-12-07 17:43:58,320 E 9832 20188] core_worker_process.cc:837: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14\u001b[32m [repeated 2x across cluster]\u001b[0m\n",
|
||
"2025-12-07 17:49:32,969\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_83af0528_33_text_det_box_thresh=0.1846,text_det_thresh=0.4663,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_17-43-23\n",
|
||
"2025-12-07 17:49:32,999\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_a3a87765_35_text_det_box_thresh=0.2856,text_det_thresh=0.4501,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_17-49-32\n",
|
||
"2025-12-07 17:49:33,002\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_a3a87765_35_text_det_box_thresh=0.2856,text_det_thresh=0.4501,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_17-49-32\n",
|
||
"2025-12-07 17:49:37,086\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_12cbaa22_34_text_det_box_thresh=0.4056,text_det_thresh=0.4728,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_17-43-28\n",
|
||
"2025-12-07 17:49:38,207\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_a3a87765_35_text_det_box_thresh=0.2856,text_det_thresh=0.4501,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_17-49-32\n",
|
||
"2025-12-07 17:49:38,207\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_a3a87765_35_text_det_box_thresh=0.2856,text_det_thresh=0.4501,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_17-49-32\n",
|
||
"2025-12-07 17:49:38,221\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_cf2bad0c_36_text_det_box_thresh=0.2837,text_det_thresh=0.5890,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_17-49-38\n",
|
||
"2025-12-07 17:49:38,224\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_cf2bad0c_36_text_det_box_thresh=0.2837,text_det_thresh=0.5890,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_17-49-38\n",
|
||
"2025-12-07 17:49:42,732\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_cf2bad0c_36_text_det_box_thresh=0.2837,text_det_thresh=0.5890,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_17-49-38\n",
|
||
"2025-12-07 17:49:42,734\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_cf2bad0c_36_text_det_box_thresh=0.2837,text_det_thresh=0.5890,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_17-49-38\n",
|
||
"\u001b[36m(trainable_paddle_ocr pid=24372)\u001b[0m [2025-12-07 17:50:08,047 E 24372 25404] core_worker_process.cc:837: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14\u001b[32m [repeated 2x across cluster]\u001b[0m\n",
|
||
"\u001b[36m(trainable_paddle_ocr pid=3272)\u001b[0m [2025-12-07 17:50:14,041 E 3272 25236] core_worker_process.cc:837: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14\n",
|
||
"2025-12-07 17:55:47,492\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_a3a87765_35_text_det_box_thresh=0.2856,text_det_thresh=0.4501,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_17-49-32\n",
|
||
"2025-12-07 17:55:47,513\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_9a9b91e7_37_text_det_box_thresh=0.3646,text_det_thresh=0.6090,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_17-55-47\n",
|
||
"2025-12-07 17:55:47,515\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_9a9b91e7_37_text_det_box_thresh=0.3646,text_det_thresh=0.6090,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_17-55-47\n",
|
||
"2025-12-07 17:55:48,925\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_cf2bad0c_36_text_det_box_thresh=0.2837,text_det_thresh=0.5890,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_17-49-38\n",
|
||
"2025-12-07 17:55:52,512\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_9a9b91e7_37_text_det_box_thresh=0.3646,text_det_thresh=0.6090,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_17-55-47\n",
|
||
"2025-12-07 17:55:52,520\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_9a9b91e7_37_text_det_box_thresh=0.3646,text_det_thresh=0.6090,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_17-55-47\n",
|
||
"2025-12-07 17:55:52,532\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_e326d901_38_text_det_box_thresh=0.3735,text_det_thresh=0.5932,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_17-55-52\n",
|
||
"2025-12-07 17:55:52,532\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_e326d901_38_text_det_box_thresh=0.3735,text_det_thresh=0.5932,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_17-55-52\n",
|
||
"2025-12-07 17:55:56,990\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_e326d901_38_text_det_box_thresh=0.3735,text_det_thresh=0.5932,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_17-55-52\n",
|
||
"2025-12-07 17:55:56,990\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_e326d901_38_text_det_box_thresh=0.3735,text_det_thresh=0.5932,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_17-55-52\n",
|
||
"\u001b[36m(trainable_paddle_ocr pid=2272)\u001b[0m [2025-12-07 17:56:22,469 E 2272 9344] core_worker_process.cc:837: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14\n",
|
||
"2025-12-07 18:01:56,576\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_9a9b91e7_37_text_det_box_thresh=0.3646,text_det_thresh=0.6090,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_17-55-47\n",
|
||
"2025-12-07 18:01:56,635\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_ccb3f19a_39_text_det_box_thresh=0.4538,text_det_thresh=0.6866,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_18-01-56\n",
|
||
"2025-12-07 18:01:56,637\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_ccb3f19a_39_text_det_box_thresh=0.4538,text_det_thresh=0.6866,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_18-01-56\n",
|
||
"2025-12-07 18:02:02,426\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_ccb3f19a_39_text_det_box_thresh=0.4538,text_det_thresh=0.6866,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_18-01-56\n",
|
||
"2025-12-07 18:02:02,426\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_ccb3f19a_39_text_det_box_thresh=0.4538,text_det_thresh=0.6866,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_18-01-56\n",
|
||
"2025-12-07 18:02:02,442\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_e326d901_38_text_det_box_thresh=0.3735,text_det_thresh=0.5932,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_17-55-52\n",
|
||
"2025-12-07 18:02:02,471\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_8c12c55f_40_text_det_box_thresh=0.4444,text_det_thresh=0.6710,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_18-02-02\n",
|
||
"2025-12-07 18:02:02,472\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_8c12c55f_40_text_det_box_thresh=0.4444,text_det_thresh=0.6710,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_18-02-02\n",
|
||
"2025-12-07 18:02:06,950\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_8c12c55f_40_text_det_box_thresh=0.4444,text_det_thresh=0.6710,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_18-02-02\n",
|
||
"2025-12-07 18:02:06,950\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_8c12c55f_40_text_det_box_thresh=0.4444,text_det_thresh=0.6710,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_18-02-02\n",
|
||
"\u001b[36m(trainable_paddle_ocr pid=1104)\u001b[0m [2025-12-07 18:02:31,870 E 1104 11720] core_worker_process.cc:837: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14\u001b[32m [repeated 2x across cluster]\u001b[0m\n",
|
||
"\u001b[36m(trainable_paddle_ocr pid=19700)\u001b[0m [2025-12-07 18:02:38,333 E 19700 6824] core_worker_process.cc:837: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14\n",
|
||
"2025-12-07 18:08:07,593\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_ccb3f19a_39_text_det_box_thresh=0.4538,text_det_thresh=0.6866,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_18-01-56\n",
|
||
"2025-12-07 18:08:07,628\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_5a62d5b6_41_text_det_box_thresh=0.2010,text_det_thresh=0.4041,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_18-08-07\n",
|
||
"2025-12-07 18:08:07,630\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_5a62d5b6_41_text_det_box_thresh=0.2010,text_det_thresh=0.4041,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_18-08-07\n",
|
||
"2025-12-07 18:08:10,260\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_8c12c55f_40_text_det_box_thresh=0.4444,text_det_thresh=0.6710,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_18-02-02\n",
|
||
"2025-12-07 18:08:12,660\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_5a62d5b6_41_text_det_box_thresh=0.2010,text_det_thresh=0.4041,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_18-08-07\n",
|
||
"2025-12-07 18:08:12,664\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_5a62d5b6_41_text_det_box_thresh=0.2010,text_det_thresh=0.4041,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_18-08-07\n",
|
||
"2025-12-07 18:08:12,675\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_bb4495b7_42_text_det_box_thresh=0.5764,text_det_thresh=0.3907,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_18-08-12\n",
|
||
"2025-12-07 18:08:12,684\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_bb4495b7_42_text_det_box_thresh=0.5764,text_det_thresh=0.3907,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_18-08-12\n",
|
||
"2025-12-07 18:08:17,160\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_bb4495b7_42_text_det_box_thresh=0.5764,text_det_thresh=0.3907,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_18-08-12\n",
|
||
"2025-12-07 18:08:17,164\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_bb4495b7_42_text_det_box_thresh=0.5764,text_det_thresh=0.3907,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_18-08-12\n",
|
||
"\u001b[36m(trainable_paddle_ocr pid=26528)\u001b[0m [2025-12-07 18:08:42,646 E 26528 5412] core_worker_process.cc:837: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14\n",
|
||
"\u001b[36m(trainable_paddle_ocr pid=21772)\u001b[0m [2025-12-07 18:08:48,607 E 21772 12564] core_worker_process.cc:837: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14\n",
|
||
"2025-12-07 18:14:33,027\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_5a62d5b6_41_text_det_box_thresh=0.2010,text_det_thresh=0.4041,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_18-08-07\n",
|
||
"2025-12-07 18:14:33,082\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_9d90711d_43_text_det_box_thresh=0.5412,text_det_thresh=0.4690,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_18-14-33\n",
|
||
"2025-12-07 18:14:33,085\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_9d90711d_43_text_det_box_thresh=0.5412,text_det_thresh=0.4690,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_18-14-33\n",
|
||
"2025-12-07 18:14:33,144\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_bb4495b7_42_text_det_box_thresh=0.5764,text_det_thresh=0.3907,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_18-08-12\n",
|
||
"2025-12-07 18:14:38,712\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_9d90711d_43_text_det_box_thresh=0.5412,text_det_thresh=0.4690,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_18-14-33\n",
|
||
"2025-12-07 18:14:38,714\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_9d90711d_43_text_det_box_thresh=0.5412,text_det_thresh=0.4690,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_18-14-33\n",
|
||
"2025-12-07 18:14:38,727\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_daaec3f8_44_text_det_box_thresh=0.5213,text_det_thresh=0.4744,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_18-14-38\n",
|
||
"2025-12-07 18:14:38,731\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_daaec3f8_44_text_det_box_thresh=0.5213,text_det_thresh=0.4744,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_18-14-38\n",
|
||
"2025-12-07 18:14:43,202\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_daaec3f8_44_text_det_box_thresh=0.5213,text_det_thresh=0.4744,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_18-14-38\n",
|
||
"2025-12-07 18:14:43,206\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_daaec3f8_44_text_det_box_thresh=0.5213,text_det_thresh=0.4744,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_18-14-38\n",
|
||
"\u001b[36m(trainable_paddle_ocr pid=17592)\u001b[0m [2025-12-07 18:15:08,237 E 17592 11980] core_worker_process.cc:837: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14\n",
|
||
"\u001b[36m(trainable_paddle_ocr pid=21292)\u001b[0m [2025-12-07 18:15:13,513 E 21292 10368] core_worker_process.cc:837: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14\n",
|
||
"2025-12-07 18:20:44,494\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_9d90711d_43_text_det_box_thresh=0.5412,text_det_thresh=0.4690,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_18-14-33\n",
|
||
"2025-12-07 18:20:44,525\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_51fb5915_45_text_det_box_thresh=0.5811,text_det_thresh=0.4854,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_18-20-44\n",
|
||
"2025-12-07 18:20:44,528\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_51fb5915_45_text_det_box_thresh=0.5811,text_det_thresh=0.4854,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_18-20-44\n",
|
||
"2025-12-07 18:20:46,235\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_daaec3f8_44_text_det_box_thresh=0.5213,text_det_thresh=0.4744,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_18-14-38\n",
|
||
"2025-12-07 18:20:49,638\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_51fb5915_45_text_det_box_thresh=0.5811,text_det_thresh=0.4854,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_18-20-44\n",
|
||
"2025-12-07 18:20:49,639\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_51fb5915_45_text_det_box_thresh=0.5811,text_det_thresh=0.4854,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_18-20-44\n",
|
||
"2025-12-07 18:20:49,649\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_18966a33_46_text_det_box_thresh=0.5133,text_det_thresh=0.5502,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_18-20-49\n",
|
||
"2025-12-07 18:20:49,649\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_18966a33_46_text_det_box_thresh=0.5133,text_det_thresh=0.5502,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_18-20-49\n",
|
||
"2025-12-07 18:20:54,162\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_18966a33_46_text_det_box_thresh=0.5133,text_det_thresh=0.5502,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_18-20-49\n",
|
||
"2025-12-07 18:20:54,162\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_18966a33_46_text_det_box_thresh=0.5133,text_det_thresh=0.5502,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_18-20-49\n",
|
||
"\u001b[36m(trainable_paddle_ocr pid=21772)\u001b[0m [2025-12-07 18:21:19,532 E 21772 9096] core_worker_process.cc:837: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14\n",
|
||
"2025-12-07 18:26:53,700\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_51fb5915_45_text_det_box_thresh=0.5811,text_det_thresh=0.4854,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_18-20-44\n",
|
||
"2025-12-07 18:26:53,763\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_b67080f9_47_text_det_box_thresh=0.5761,text_det_thresh=0.5534,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_18-26-53\n",
|
||
"2025-12-07 18:26:53,766\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_b67080f9_47_text_det_box_thresh=0.5761,text_det_thresh=0.5534,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_18-26-53\n",
|
||
"2025-12-07 18:26:57,513\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_18966a33_46_text_det_box_thresh=0.5133,text_det_thresh=0.5502,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_18-20-49\n",
|
||
"2025-12-07 18:26:59,363\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_b67080f9_47_text_det_box_thresh=0.5761,text_det_thresh=0.5534,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_18-26-53\n",
|
||
"2025-12-07 18:26:59,363\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_b67080f9_47_text_det_box_thresh=0.5761,text_det_thresh=0.5534,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_18-26-53\n",
|
||
"2025-12-07 18:26:59,379\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_2533f368_48_text_det_box_thresh=0.5246,text_det_thresh=0.5572,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_18-26-59\n",
|
||
"2025-12-07 18:26:59,382\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_2533f368_48_text_det_box_thresh=0.5246,text_det_thresh=0.5572,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_18-26-59\n",
|
||
"2025-12-07 18:27:03,913\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_2533f368_48_text_det_box_thresh=0.5246,text_det_thresh=0.5572,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_18-26-59\n",
|
||
"2025-12-07 18:27:03,913\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_2533f368_48_text_det_box_thresh=0.5246,text_det_thresh=0.5572,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_18-26-59\n",
|
||
"\u001b[36m(trainable_paddle_ocr pid=20948)\u001b[0m [2025-12-07 18:27:29,044 E 20948 19656] core_worker_process.cc:837: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14\u001b[32m [repeated 2x across cluster]\u001b[0m\n",
|
||
"\u001b[36m(trainable_paddle_ocr pid=11208)\u001b[0m [2025-12-07 18:27:34,203 E 11208 2320] core_worker_process.cc:837: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14\n",
|
||
"2025-12-07 18:33:05,400\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_b67080f9_47_text_det_box_thresh=0.5761,text_det_thresh=0.5534,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_18-26-53\n",
|
||
"2025-12-07 18:33:05,427\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_451d018d_49_text_det_box_thresh=0.5495,text_det_thresh=0.6340,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_18-33-05\n",
|
||
"2025-12-07 18:33:05,428\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_451d018d_49_text_det_box_thresh=0.5495,text_det_thresh=0.6340,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_18-33-05\n",
|
||
"2025-12-07 18:33:10,740\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_451d018d_49_text_det_box_thresh=0.5495,text_det_thresh=0.6340,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_18-33-05\n",
|
||
"2025-12-07 18:33:10,743\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_451d018d_49_text_det_box_thresh=0.5495,text_det_thresh=0.6340,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_18-33-05\n",
|
||
"2025-12-07 18:33:15,130\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_2533f368_48_text_det_box_thresh=0.5246,text_det_thresh=0.5572,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_18-26-59\n",
|
||
"2025-12-07 18:33:15,154\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_2256e752_50_text_det_box_thresh=0.6229,text_det_thresh=0.6478,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_18-33-15\n",
|
||
"2025-12-07 18:33:15,156\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_2256e752_50_text_det_box_thresh=0.6229,text_det_thresh=0.6478,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_18-33-15\n",
|
||
"2025-12-07 18:33:19,685\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_2256e752_50_text_det_box_thresh=0.6229,text_det_thresh=0.6478,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_18-33-15\n",
|
||
"2025-12-07 18:33:19,685\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_2256e752_50_text_det_box_thresh=0.6229,text_det_thresh=0.6478,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_18-33-15\n",
|
||
"\u001b[36m(trainable_paddle_ocr pid=3616)\u001b[0m [2025-12-07 18:33:40,534 E 3616 22824] core_worker_process.cc:837: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14\n",
|
||
"\u001b[36m(trainable_paddle_ocr pid=25468)\u001b[0m [2025-12-07 18:33:49,934 E 25468 7192] core_worker_process.cc:837: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14\n",
|
||
"2025-12-07 18:39:29,627\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_2256e752_50_text_det_box_thresh=0.6229,text_det_thresh=0.6478,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_18-33-15\n",
|
||
"2025-12-07 18:39:29,649\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_451d018d_49_text_det_box_thresh=0.5495,text_det_thresh=0.6340,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_18-33-05\n",
|
||
"2025-12-07 18:39:29,687\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_0a892729_51_text_det_box_thresh=0.5429,text_det_thresh=0.4217,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_18-39-29\n",
|
||
"2025-12-07 18:39:29,690\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_0a892729_51_text_det_box_thresh=0.5429,text_det_thresh=0.4217,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_18-39-29\n",
|
||
"2025-12-07 18:39:35,040\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_0a892729_51_text_det_box_thresh=0.5429,text_det_thresh=0.4217,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_18-39-29\n",
|
||
"2025-12-07 18:39:35,040\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_0a892729_51_text_det_box_thresh=0.5429,text_det_thresh=0.4217,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_18-39-29\n",
|
||
"2025-12-07 18:39:35,057\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_495075f5_52_text_det_box_thresh=0.6319,text_det_thresh=0.4187,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_18-39-35\n",
|
||
"2025-12-07 18:39:35,059\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_495075f5_52_text_det_box_thresh=0.6319,text_det_thresh=0.4187,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_18-39-35\n",
|
||
"2025-12-07 18:39:39,597\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_495075f5_52_text_det_box_thresh=0.6319,text_det_thresh=0.4187,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_18-39-35\n",
|
||
"2025-12-07 18:39:39,598\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_495075f5_52_text_det_box_thresh=0.6319,text_det_thresh=0.4187,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_18-39-35\n",
|
||
"\u001b[36m(trainable_paddle_ocr pid=26212)\u001b[0m [2025-12-07 18:40:04,811 E 26212 22100] core_worker_process.cc:837: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14\n",
|
||
"\u001b[36m(trainable_paddle_ocr pid=23604)\u001b[0m [2025-12-07 18:40:10,081 E 23604 16924] core_worker_process.cc:837: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14\n",
|
||
"2025-12-07 18:45:42,301\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_0a892729_51_text_det_box_thresh=0.5429,text_det_thresh=0.4217,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_18-39-29\n",
|
||
"2025-12-07 18:45:42,331\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_54c45552_53_text_det_box_thresh=0.6197,text_det_thresh=0.4638,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_18-45-42\n",
|
||
"2025-12-07 18:45:42,335\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_54c45552_53_text_det_box_thresh=0.6197,text_det_thresh=0.4638,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_18-45-42\n",
|
||
"2025-12-07 18:45:45,144\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_495075f5_52_text_det_box_thresh=0.6319,text_det_thresh=0.4187,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_18-39-35\n",
|
||
"2025-12-07 18:45:47,422\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_54c45552_53_text_det_box_thresh=0.6197,text_det_thresh=0.4638,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_18-45-42\n",
|
||
"2025-12-07 18:45:47,422\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_54c45552_53_text_det_box_thresh=0.6197,text_det_thresh=0.4638,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_18-45-42\n",
|
||
"2025-12-07 18:45:47,436\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_6b2e9b93_54_text_det_box_thresh=0.4893,text_det_thresh=0.4752,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_18-45-47\n",
|
||
"2025-12-07 18:45:47,436\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_6b2e9b93_54_text_det_box_thresh=0.4893,text_det_thresh=0.4752,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_18-45-47\n",
|
||
"2025-12-07 18:45:51,980\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_6b2e9b93_54_text_det_box_thresh=0.4893,text_det_thresh=0.4752,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_18-45-47\n",
|
||
"2025-12-07 18:45:51,980\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_6b2e9b93_54_text_det_box_thresh=0.4893,text_det_thresh=0.4752,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_18-45-47\n",
|
||
"\u001b[36m(trainable_paddle_ocr pid=25352)\u001b[0m [2025-12-07 18:46:17,386 E 25352 26068] core_worker_process.cc:837: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14\n",
|
||
"2025-12-07 18:51:55,425\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_54c45552_53_text_det_box_thresh=0.6197,text_det_thresh=0.4638,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_18-45-42\n",
|
||
"2025-12-07 18:51:55,497\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_e9a6b81f_55_text_det_box_thresh=0.4926,text_det_thresh=0.4879,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_18-51-55\n",
|
||
"2025-12-07 18:51:55,501\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_e9a6b81f_55_text_det_box_thresh=0.4926,text_det_thresh=0.4879,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_18-51-55\n",
|
||
"2025-12-07 18:51:57,995\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_6b2e9b93_54_text_det_box_thresh=0.4893,text_det_thresh=0.4752,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_18-45-47\n",
|
||
"2025-12-07 18:52:01,238\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_e9a6b81f_55_text_det_box_thresh=0.4926,text_det_thresh=0.4879,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_18-51-55\n",
|
||
"2025-12-07 18:52:01,239\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_e9a6b81f_55_text_det_box_thresh=0.4926,text_det_thresh=0.4879,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_18-51-55\n",
|
||
"2025-12-07 18:52:01,255\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_076c5450_56_text_det_box_thresh=0.5881,text_det_thresh=0.4884,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_18-52-01\n",
|
||
"2025-12-07 18:52:01,258\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_076c5450_56_text_det_box_thresh=0.5881,text_det_thresh=0.4884,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_18-52-01\n",
|
||
"2025-12-07 18:52:05,685\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_076c5450_56_text_det_box_thresh=0.5881,text_det_thresh=0.4884,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_18-52-01\n",
|
||
"2025-12-07 18:52:05,685\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_076c5450_56_text_det_box_thresh=0.5881,text_det_thresh=0.4884,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_18-52-01\n",
|
||
"\u001b[36m(trainable_paddle_ocr pid=4036)\u001b[0m [2025-12-07 18:52:30,776 E 4036 16404] core_worker_process.cc:837: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14\u001b[32m [repeated 2x across cluster]\u001b[0m\n",
|
||
"\u001b[36m(trainable_paddle_ocr pid=4832)\u001b[0m [2025-12-07 18:52:36,982 E 4832 22740] core_worker_process.cc:837: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14\n",
|
||
"2025-12-07 18:58:08,591\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_e9a6b81f_55_text_det_box_thresh=0.4926,text_det_thresh=0.4879,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_18-51-55\n",
|
||
"2025-12-07 18:58:08,621\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_4a42a3ea_57_text_det_box_thresh=0.5940,text_det_thresh=0.5590,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_18-58-08\n",
|
||
"2025-12-07 18:58:08,624\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_4a42a3ea_57_text_det_box_thresh=0.5940,text_det_thresh=0.5590,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_18-58-08\n",
|
||
"2025-12-07 18:58:10,886\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_076c5450_56_text_det_box_thresh=0.5881,text_det_thresh=0.4884,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_18-52-01\n",
|
||
"2025-12-07 18:58:13,816\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_4a42a3ea_57_text_det_box_thresh=0.5940,text_det_thresh=0.5590,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_18-58-08\n",
|
||
"2025-12-07 18:58:13,816\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_4a42a3ea_57_text_det_box_thresh=0.5940,text_det_thresh=0.5590,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_18-58-08\n",
|
||
"2025-12-07 18:58:13,830\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_041795f1_58_text_det_box_thresh=0.6617,text_det_thresh=0.5650,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_18-58-13\n",
|
||
"2025-12-07 18:58:13,833\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_041795f1_58_text_det_box_thresh=0.6617,text_det_thresh=0.5650,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_18-58-13\n",
|
||
"2025-12-07 18:58:18,273\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_041795f1_58_text_det_box_thresh=0.6617,text_det_thresh=0.5650,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_18-58-13\n",
|
||
"2025-12-07 18:58:18,280\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_041795f1_58_text_det_box_thresh=0.6617,text_det_thresh=0.5650,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_18-58-13\n",
|
||
"\u001b[36m(trainable_paddle_ocr pid=14912)\u001b[0m [2025-12-07 18:58:43,671 E 14912 9648] core_worker_process.cc:837: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14\n",
|
||
"2025-12-07 19:04:24,842\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_4a42a3ea_57_text_det_box_thresh=0.5940,text_det_thresh=0.5590,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_18-58-08\n",
|
||
"2025-12-07 19:04:24,907\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_8abb3f37_59_text_det_box_thresh=0.4637,text_det_thresh=0.4898,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_19-04-24\n",
|
||
"2025-12-07 19:04:24,910\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_8abb3f37_59_text_det_box_thresh=0.4637,text_det_thresh=0.4898,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_19-04-24\n",
|
||
"2025-12-07 19:04:29,252\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_041795f1_58_text_det_box_thresh=0.6617,text_det_thresh=0.5650,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_18-58-13\n",
|
||
"2025-12-07 19:04:30,602\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_8abb3f37_59_text_det_box_thresh=0.4637,text_det_thresh=0.4898,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_19-04-24\n",
|
||
"2025-12-07 19:04:30,603\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_8abb3f37_59_text_det_box_thresh=0.4637,text_det_thresh=0.4898,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_19-04-24\n",
|
||
"2025-12-07 19:04:30,613\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_f2cb682e_60_text_det_box_thresh=0.4522,text_det_thresh=0.4918,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_19-04-30\n",
|
||
"2025-12-07 19:04:30,619\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_f2cb682e_60_text_det_box_thresh=0.4522,text_det_thresh=0.4918,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_19-04-30\n",
|
||
"2025-12-07 19:04:35,119\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_f2cb682e_60_text_det_box_thresh=0.4522,text_det_thresh=0.4918,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_19-04-30\n",
|
||
"2025-12-07 19:04:35,119\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_f2cb682e_60_text_det_box_thresh=0.4522,text_det_thresh=0.4918,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_19-04-30\n",
|
||
"\u001b[36m(trainable_paddle_ocr pid=22012)\u001b[0m [2025-12-07 19:05:01,269 E 22012 4372] core_worker_process.cc:837: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14\u001b[32m [repeated 2x across cluster]\u001b[0m\n",
|
||
"2025-12-07 19:10:35,351\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_8abb3f37_59_text_det_box_thresh=0.4637,text_det_thresh=0.4898,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_19-04-24\n",
|
||
"2025-12-07 19:10:35,442\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_463fe5e7_61_text_det_box_thresh=0.5202,text_det_thresh=0.5373,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_19-10-35\n",
|
||
"2025-12-07 19:10:35,445\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_463fe5e7_61_text_det_box_thresh=0.5202,text_det_thresh=0.5373,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_19-10-35\n",
|
||
"2025-12-07 19:10:40,065\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_f2cb682e_60_text_det_box_thresh=0.4522,text_det_thresh=0.4918,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_19-04-30\n",
|
||
"2025-12-07 19:10:41,249\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_463fe5e7_61_text_det_box_thresh=0.5202,text_det_thresh=0.5373,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_19-10-35\n",
|
||
"2025-12-07 19:10:41,249\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_463fe5e7_61_text_det_box_thresh=0.5202,text_det_thresh=0.5373,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_19-10-35\n",
|
||
"2025-12-07 19:10:41,261\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_88bbe87d_62_text_det_box_thresh=0.5111,text_det_thresh=0.5275,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_19-10-41\n",
|
||
"2025-12-07 19:10:41,261\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_88bbe87d_62_text_det_box_thresh=0.5111,text_det_thresh=0.5275,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_19-10-41\n",
|
||
"2025-12-07 19:10:45,749\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_88bbe87d_62_text_det_box_thresh=0.5111,text_det_thresh=0.5275,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_19-10-41\n",
|
||
"2025-12-07 19:10:45,750\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_88bbe87d_62_text_det_box_thresh=0.5111,text_det_thresh=0.5275,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_19-10-41\n",
|
||
"\u001b[36m(trainable_paddle_ocr pid=16524)\u001b[0m [2025-12-07 19:11:10,747 E 16524 6148] core_worker_process.cc:837: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14\u001b[32m [repeated 2x across cluster]\u001b[0m\n",
|
||
"\u001b[36m(trainable_paddle_ocr pid=15084)\u001b[0m [2025-12-07 19:11:16,039 E 15084 20216] core_worker_process.cc:837: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14\n",
|
||
"2025-12-07 19:16:51,841\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_463fe5e7_61_text_det_box_thresh=0.5202,text_det_thresh=0.5373,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_19-10-35\n",
|
||
"2025-12-07 19:16:51,883\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_33ea1cc6_63_text_det_box_thresh=0.5158,text_det_thresh=0.5230,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_19-16-51\n",
|
||
"2025-12-07 19:16:51,884\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_33ea1cc6_63_text_det_box_thresh=0.5158,text_det_thresh=0.5230,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_19-16-51\n",
|
||
"2025-12-07 19:16:55,313\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_88bbe87d_62_text_det_box_thresh=0.5111,text_det_thresh=0.5275,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_19-10-41\n",
|
||
"2025-12-07 19:16:57,623\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_33ea1cc6_63_text_det_box_thresh=0.5158,text_det_thresh=0.5230,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_19-16-51\n",
|
||
"2025-12-07 19:16:57,623\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_33ea1cc6_63_text_det_box_thresh=0.5158,text_det_thresh=0.5230,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_19-16-51\n",
|
||
"2025-12-07 19:16:57,638\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_1243723e_64_text_det_box_thresh=0.5573,text_det_thresh=0.3727,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_19-16-57\n",
|
||
"2025-12-07 19:16:57,639\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_1243723e_64_text_det_box_thresh=0.5573,text_det_thresh=0.3727,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_19-16-57\n",
|
||
"2025-12-07 19:17:02,358\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_1243723e_64_text_det_box_thresh=0.5573,text_det_thresh=0.3727,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_19-16-57\n",
|
||
"2025-12-07 19:17:02,362\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_1243723e_64_text_det_box_thresh=0.5573,text_det_thresh=0.3727,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_19-16-57\n",
|
||
"\u001b[36m(trainable_paddle_ocr pid=17380)\u001b[0m [2025-12-07 19:17:27,300 E 17380 17224] core_worker_process.cc:837: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14\n",
|
||
"\u001b[36m(trainable_paddle_ocr pid=11232)\u001b[0m [2025-12-07 19:17:32,685 E 11232 7916] core_worker_process.cc:837: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14\n",
|
||
"2025-12-07 19:23:14,420\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_33ea1cc6_63_text_det_box_thresh=0.5158,text_det_thresh=0.5230,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_19-16-51\n",
|
||
"2025-12-07 19:23:17,826\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-07_15-57-58_291425_24012\\artifacts\\2025-12-07_16-03-56\\trainable_paddle_ocr_2025-12-07_16-03-56\\driver_artifacts\\trainable_paddle_ocr_1243723e_64_text_det_box_thresh=0.5573,text_det_thresh=0.3727,text_det_unclip_ratio=0.0000,text_rec_score_thr_2025-12-07_19-16-57\n",
|
||
"2025-12-07 19:23:17,928\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to 'C:/Users/Sergio/ray_results/trainable_paddle_ocr_2025-12-07_16-03-56' in 0.0859s.\n",
|
||
"2025-12-07 19:23:17,957\tINFO tune.py:1041 -- Total run time: 11961.30 seconds (11961.14 seconds for the tuning loop).\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"from ray.tune.search.optuna import OptunaSearch\n",
|
||
"\n",
|
||
"def trainable_paddle_ocr(config):\n",
|
||
" args = [sys.executable, SCRIPT_ABS, \"--pdf-folder\", PDF_FOLDER_ABS]\n",
|
||
" for k, v in config.items():\n",
|
||
" args += [f\"--{KEYMAP[k]}\", str(v)]\n",
|
||
" proc = subprocess.run(args, capture_output=True, text=True, cwd=SCRIPT_DIR)\n",
|
||
"\n",
|
||
" if proc.returncode != 0:\n",
|
||
" tune.report({\"CER\": 1.0, \"WER\": 1.0, \"TIME\": 0.0, 'PAGES': 0, 'TIME_PER_PAGE': 0, \"ERROR\": proc.stderr[:500]})\n",
|
||
" return\n",
|
||
" # last line contains the metrics in json format\n",
|
||
" last = proc.stdout.strip().splitlines()[-1]\n",
|
||
" \n",
|
||
" metrics = json.loads(last)\n",
|
||
" tune.report(metrics=metrics)\n",
|
||
"\n",
|
||
"tuner = tune.Tuner(\n",
|
||
" trainable_paddle_ocr,\n",
|
||
" tune_config=tune.TuneConfig(metric=\"CER\", \n",
|
||
" mode=\"min\", \n",
|
||
" search_alg=OptunaSearch(),\n",
|
||
" num_samples=64, \n",
|
||
" max_concurrent_trials=2),\n",
|
||
" run_config=air.RunConfig(verbose=2, log_to_file=False),\n",
|
||
" param_space=search_space\n",
|
||
")\n",
|
||
"\n",
|
||
"results = tuner.fit()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "710a67ce",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"df = results.get_dataframe()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "1ab345a3",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Guardado: raytune_paddle_subproc_results_20251207_192320.csv\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# Generate a unique filename with timestamp\n",
|
||
"timestamp = datetime.now().strftime(\"%Y%m%d_%H%M%S\")\n",
|
||
"filename = f\"raytune_paddle_subproc_results_{timestamp}.csv\"\n",
|
||
"filepath = os.path.join(OUTPUT_FOLDER, filename)\n",
|
||
"\n",
|
||
"\n",
|
||
"df.to_csv(filename, index=False)\n",
|
||
"print(f\"Guardado: {filename}\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "3e3a34e4",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>CER</th>\n",
|
||
" <th>WER</th>\n",
|
||
" <th>TIME</th>\n",
|
||
" <th>PAGES</th>\n",
|
||
" <th>TIME_PER_PAGE</th>\n",
|
||
" <th>timestamp</th>\n",
|
||
" <th>checkpoint_dir_name</th>\n",
|
||
" <th>training_iteration</th>\n",
|
||
" <th>time_this_iter_s</th>\n",
|
||
" <th>time_total_s</th>\n",
|
||
" <th>pid</th>\n",
|
||
" <th>time_since_restore</th>\n",
|
||
" <th>iterations_since_restore</th>\n",
|
||
" <th>config/text_det_thresh</th>\n",
|
||
" <th>config/text_det_box_thresh</th>\n",
|
||
" <th>config/text_det_unclip_ratio</th>\n",
|
||
" <th>config/text_rec_score_thresh</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>count</th>\n",
|
||
" <td>64.000000</td>\n",
|
||
" <td>64.000000</td>\n",
|
||
" <td>64.000000</td>\n",
|
||
" <td>64.0</td>\n",
|
||
" <td>64.000000</td>\n",
|
||
" <td>6.400000e+01</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>64.0</td>\n",
|
||
" <td>64.000000</td>\n",
|
||
" <td>64.000000</td>\n",
|
||
" <td>64.000000</td>\n",
|
||
" <td>64.000000</td>\n",
|
||
" <td>64.0</td>\n",
|
||
" <td>64.000000</td>\n",
|
||
" <td>64.000000</td>\n",
|
||
" <td>64.0</td>\n",
|
||
" <td>64.000000</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>mean</th>\n",
|
||
" <td>0.052482</td>\n",
|
||
" <td>0.142770</td>\n",
|
||
" <td>347.605870</td>\n",
|
||
" <td>5.0</td>\n",
|
||
" <td>69.423734</td>\n",
|
||
" <td>1.765126e+09</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>367.715945</td>\n",
|
||
" <td>367.715945</td>\n",
|
||
" <td>16306.750000</td>\n",
|
||
" <td>367.715945</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>0.419091</td>\n",
|
||
" <td>0.392965</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.470584</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>std</th>\n",
|
||
" <td>0.110269</td>\n",
|
||
" <td>0.107515</td>\n",
|
||
" <td>7.876539</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>1.574470</td>\n",
|
||
" <td>3.473487e+03</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>8.011554</td>\n",
|
||
" <td>8.011554</td>\n",
|
||
" <td>8179.917114</td>\n",
|
||
" <td>8.011554</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.167178</td>\n",
|
||
" <td>0.195419</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.219216</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>min</th>\n",
|
||
" <td>0.011535</td>\n",
|
||
" <td>0.098902</td>\n",
|
||
" <td>320.966205</td>\n",
|
||
" <td>5.0</td>\n",
|
||
" <td>64.095210</td>\n",
|
||
" <td>1.765120e+09</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>341.071264</td>\n",
|
||
" <td>341.071264</td>\n",
|
||
" <td>1104.000000</td>\n",
|
||
" <td>341.071264</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>0.016997</td>\n",
|
||
" <td>0.000242</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.002891</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>25%</th>\n",
|
||
" <td>0.011968</td>\n",
|
||
" <td>0.100441</td>\n",
|
||
" <td>344.239116</td>\n",
|
||
" <td>5.0</td>\n",
|
||
" <td>68.755118</td>\n",
|
||
" <td>1.765123e+09</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>364.708660</td>\n",
|
||
" <td>364.708660</td>\n",
|
||
" <td>9272.000000</td>\n",
|
||
" <td>364.708660</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>0.328652</td>\n",
|
||
" <td>0.230515</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.311325</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>50%</th>\n",
|
||
" <td>0.012314</td>\n",
|
||
" <td>0.102033</td>\n",
|
||
" <td>346.419682</td>\n",
|
||
" <td>5.0</td>\n",
|
||
" <td>69.188875</td>\n",
|
||
" <td>1.765126e+09</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>366.103412</td>\n",
|
||
" <td>366.103412</td>\n",
|
||
" <td>18522.000000</td>\n",
|
||
" <td>366.103412</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>0.465068</td>\n",
|
||
" <td>0.448332</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.559640</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>75%</th>\n",
|
||
" <td>0.040339</td>\n",
|
||
" <td>0.132047</td>\n",
|
||
" <td>350.144563</td>\n",
|
||
" <td>5.0</td>\n",
|
||
" <td>69.930173</td>\n",
|
||
" <td>1.765129e+09</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>370.648662</td>\n",
|
||
" <td>370.648662</td>\n",
|
||
" <td>23167.000000</td>\n",
|
||
" <td>370.648662</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>0.530501</td>\n",
|
||
" <td>0.544563</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.645015</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>max</th>\n",
|
||
" <td>0.516069</td>\n",
|
||
" <td>0.594530</td>\n",
|
||
" <td>368.571180</td>\n",
|
||
" <td>5.0</td>\n",
|
||
" <td>73.625040</td>\n",
|
||
" <td>1.765132e+09</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>388.150608</td>\n",
|
||
" <td>388.150608</td>\n",
|
||
" <td>26528.000000</td>\n",
|
||
" <td>388.150608</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>0.686641</td>\n",
|
||
" <td>0.690232</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.699247</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" CER WER TIME PAGES TIME_PER_PAGE timestamp \\\n",
|
||
"count 64.000000 64.000000 64.000000 64.0 64.000000 6.400000e+01 \n",
|
||
"mean 0.052482 0.142770 347.605870 5.0 69.423734 1.765126e+09 \n",
|
||
"std 0.110269 0.107515 7.876539 0.0 1.574470 3.473487e+03 \n",
|
||
"min 0.011535 0.098902 320.966205 5.0 64.095210 1.765120e+09 \n",
|
||
"25% 0.011968 0.100441 344.239116 5.0 68.755118 1.765123e+09 \n",
|
||
"50% 0.012314 0.102033 346.419682 5.0 69.188875 1.765126e+09 \n",
|
||
"75% 0.040339 0.132047 350.144563 5.0 69.930173 1.765129e+09 \n",
|
||
"max 0.516069 0.594530 368.571180 5.0 73.625040 1.765132e+09 \n",
|
||
"\n",
|
||
" checkpoint_dir_name training_iteration time_this_iter_s \\\n",
|
||
"count 0.0 64.0 64.000000 \n",
|
||
"mean NaN 1.0 367.715945 \n",
|
||
"std NaN 0.0 8.011554 \n",
|
||
"min NaN 1.0 341.071264 \n",
|
||
"25% NaN 1.0 364.708660 \n",
|
||
"50% NaN 1.0 366.103412 \n",
|
||
"75% NaN 1.0 370.648662 \n",
|
||
"max NaN 1.0 388.150608 \n",
|
||
"\n",
|
||
" time_total_s pid time_since_restore \\\n",
|
||
"count 64.000000 64.000000 64.000000 \n",
|
||
"mean 367.715945 16306.750000 367.715945 \n",
|
||
"std 8.011554 8179.917114 8.011554 \n",
|
||
"min 341.071264 1104.000000 341.071264 \n",
|
||
"25% 364.708660 9272.000000 364.708660 \n",
|
||
"50% 366.103412 18522.000000 366.103412 \n",
|
||
"75% 370.648662 23167.000000 370.648662 \n",
|
||
"max 388.150608 26528.000000 388.150608 \n",
|
||
"\n",
|
||
" iterations_since_restore config/text_det_thresh \\\n",
|
||
"count 64.0 64.000000 \n",
|
||
"mean 1.0 0.419091 \n",
|
||
"std 0.0 0.167178 \n",
|
||
"min 1.0 0.016997 \n",
|
||
"25% 1.0 0.328652 \n",
|
||
"50% 1.0 0.465068 \n",
|
||
"75% 1.0 0.530501 \n",
|
||
"max 1.0 0.686641 \n",
|
||
"\n",
|
||
" config/text_det_box_thresh config/text_det_unclip_ratio \\\n",
|
||
"count 64.000000 64.0 \n",
|
||
"mean 0.392965 0.0 \n",
|
||
"std 0.195419 0.0 \n",
|
||
"min 0.000242 0.0 \n",
|
||
"25% 0.230515 0.0 \n",
|
||
"50% 0.448332 0.0 \n",
|
||
"75% 0.544563 0.0 \n",
|
||
"max 0.690232 0.0 \n",
|
||
"\n",
|
||
" config/text_rec_score_thresh \n",
|
||
"count 64.000000 \n",
|
||
"mean 0.470584 \n",
|
||
"std 0.219216 \n",
|
||
"min 0.002891 \n",
|
||
"25% 0.311325 \n",
|
||
"50% 0.559640 \n",
|
||
"75% 0.645015 \n",
|
||
"max 0.699247 "
|
||
]
|
||
},
|
||
"execution_count": 32,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"#df = pd.read_csv(\"raytune_paddle_subproc_results_20251207_192320.csv\")\n",
|
||
"df.describe()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 33,
|
||
"id": "50fa5b59",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Correlación con CER:\n",
|
||
" CER 1.000000\n",
|
||
"config/text_det_box_thresh 0.226375\n",
|
||
"config/text_rec_score_thresh -0.160833\n",
|
||
"config/text_det_thresh -0.522705\n",
|
||
"config/text_det_unclip_ratio NaN\n",
|
||
"Name: CER, dtype: float64\n",
|
||
"Correlación con WER:\n",
|
||
" WER 1.000000\n",
|
||
"config/text_det_box_thresh 0.226714\n",
|
||
"config/text_rec_score_thresh -0.172597\n",
|
||
"config/text_det_thresh -0.521391\n",
|
||
"config/text_det_unclip_ratio NaN\n",
|
||
"Name: WER, dtype: float64\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"param_cols = [\n",
|
||
" \"config/text_det_thresh\",\n",
|
||
" \"config/text_det_box_thresh\",\n",
|
||
" \"config/text_det_unclip_ratio\",\n",
|
||
" \"config/text_rec_score_thresh\",\n",
|
||
"]\n",
|
||
"labels = [\n",
|
||
" \"Detection Pixel Threshold\",\n",
|
||
" \"Detection Box Threshold\",\n",
|
||
" \"Unclip Ratio\",\n",
|
||
" \"Recognition Score Threshold\",\n",
|
||
"]\n",
|
||
"\n",
|
||
"# Correlación de Pearson con CER y WER\n",
|
||
"corr_cer = df[param_cols + [\"CER\"]].corr()[\"CER\"].sort_values(ascending=False)\n",
|
||
"corr_wer = df[param_cols + [\"WER\"]].corr()[\"WER\"].sort_values(ascending=False)\n",
|
||
"\n",
|
||
"print(\"Correlación con CER:\\n\", corr_cer)\n",
|
||
"print(\"Correlación con WER:\\n\", corr_wer)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 34,
|
||
"id": "9462b7a2",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"<Axes: xlabel='config/textline_orientation', ylabel='WER'>"
|
||
]
|
||
},
|
||
"execution_count": 34,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 1000x400 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"# Direct comparison for binary parameter\n",
|
||
"#print(\"textline_orientation=True:\")\n",
|
||
"#print(df[df[\"config/textline_orientation\"] == True][[\"CER\", \"WER\"]].describe())\n",
|
||
"\n",
|
||
"#print(\"\\ntextline_orientation=False:\")\n",
|
||
"#print(df[df[\"config/textline_orientation\"] == False][[\"CER\", \"WER\"]].describe())\n",
|
||
"\n",
|
||
"# Or a simple mean comparison\n",
|
||
"df.groupby(\"config/textline_orientation\")[[\"CER\", \"WER\"]].mean()\n",
|
||
"\n",
|
||
"import seaborn as sns\n",
|
||
"fig, axes = plt.subplots(1, 2, figsize=(10, 4))\n",
|
||
"sns.boxplot(data=df, x=\"config/textline_orientation\", y=\"CER\", ax=axes[0])\n",
|
||
"sns.boxplot(data=df, x=\"config/textline_orientation\", y=\"WER\", ax=axes[1])"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "bc78df46",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Interpretation:\n",
|
||
"\n",
|
||
"7x better CER with textline_orientation=True. And the variance is much tighter — more reliable results.\n",
|
||
"For Spanish business documents with mixed layouts (tables, headers, addresses), orientation classification helps PaddleOCR correctly order text lines. Makes sense.\n",
|
||
"This is thesis-worthy: a single boolean flag accounts for more improvement than all the continuous hyperparameters combined. You could argue that for document OCR pipelines, architectural choices (orientation classification) matter more than threshold tuning."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 30,
|
||
"id": "02fc0a87",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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|
||
"text/plain": [
|
||
"<Figure size 1200x1000 with 4 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"fig, axes = plt.subplots(2, 2, figsize=(12, 10))\n",
|
||
"\n",
|
||
"for ax, col, label in zip(axes.flat, param_cols, labels):\n",
|
||
" ax.scatter(df[col], df[\"CER\"], alpha=0.6)\n",
|
||
" ax.set_xlabel(label)\n",
|
||
" ax.set_ylabel(\"CER\")\n",
|
||
" ax.set_title(f\"Effect of {label} on CER\")\n",
|
||
"\n",
|
||
"plt.tight_layout()\n",
|
||
"plt.savefig(\"hyperparameter_analysis_cer.png\", dpi=150)\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 31,
|
||
"id": "cc1e3d53",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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gOXga6jt1PgBwUjLEUDrcQ8+ua0CtB0AikQfRlqkpJZZnvzPVOWl+yctMMWd3tbeVHojrsloeMLQ8u98au6eAHphoEinydaJZVmf1vokmuaUHEBkpPhnS+6thi1wlK3odTUAmamFkN/XOikU7Ozms06nvSLSvpds4TXRnp/nF6/GY30BD0JKyQEC6WakmER75HbKXqxem0ESGDru2e7nqNly3xzpdX7et9xLN+j23fKtYYkXdXpHLHNQrS97fUi6BYFjyMxuTSTrkW5fQNzddKuuDsnl3jRx9WDezbF2WXnFbE0J6EkS32yMK8+XDrY09wSrqgk1JLN0naBJpRP88ueXlT2TJ+jKzfa8PfnUdb5+3MaG0amu5pPp9cnhBtuyqrpdtlbUSDDXOo+ui+z/d3tQEgmbYuvZ00uySrocmmXS/NqQg2ySOauoazOek+6O6YEhe+3iH1DWETNJLl6PvVRet67F2Z7VkpTYmu3LTG5Ni73ymvalC8suThsiGXdVy84sfm8/b7IczdD8cls2795npyunEVHz6Z33pD3/4g1x88cUyZcoUGTp0qAmsMjMz5fHHH291/mOOOcacETznnHMkLS3N8fUFkDzmfrC7U+cDACclQwyl9Ud0uEeK12sOZvRssB4k6a3e92lvKZ9HctIbE05NPB4p6Zltzs5X14dMjystkF5dFzQHFxqItzy7fyA6jx6863P0ubqM9iyrqffNAXrV6nR9/GC9bw6W3NIDFz0w0SsUHiz5hbYTkHpQqd+zSHYCckhBTlTJzPZ+frH6fOyeIZpUzc9IleKeWeZW7+t0fTyZxKKdnfrsnPyORPNan+6olCcXbzJJHE10jC/pIccM7C7HFHeT44f0lIKcNPlgS4VJ9rRcrm6HKuuCpjfQV8v3mPs6XbdXbb2XaNZvVWmFrCqtjLq9IpfZuF/4av004aO9iPRnr7kjnV5RGzTbZX1ce0O9+/kesw3QbYQmvnrkpMnIw7pJ3/x02RcImiGOe/fVm7aye60uXLtTNpY1T0gpvYLsvkDIvG5dQ1A+2lZhepHZCanIK32XVQekNhA2ybxgKGyG3Ok+Tt+y7ts++qLSJP8KctMkaFmyaU9NU480XYbumjShqE+wR97p+6wPBs19j8fbtA/V9/fs8q3y4ML1JiGly8xI9YvX6zW3el+nP/zmZ2ZonyuSUoFAwHQhnzhx4lcr4/Wa+0uXLu2016mvr5fKyspmfwAQ7abW2U0yALgnhtKDf60/0j07VbplppgzvXqQoLfdM1MlPyvV9IjytzggUd2yUmVQQbbkZvjNWeVNZTVSXtt4cNXeITA6rz7nqKI8s4z2LCuy901rou19c7Dklibo9KBFz8B3JPnlZh1NQLals5KT7dGyt4keSOuVK01PkoLspt4qkUmFri4W7ezUZ+fkdySa19KkzIaymq+SPib5nSLds9NM0qm1JJm9XN0O6W9HL0wRSe+bK6h6PG2+l2jWT7fruv2Mtr0il6knNiLXL/I3oP/X34k+rvMpHVan24FuGV/VNrT3M2MGdJcxAzQ5lSEXH1civ/nukbJyS7npLaa9qnS7bPNE/JnXshoTVJq0ipit2TxfjroTTSw1hMNm/5fq9Uh6is/MpL2oUn2apGocEqi/6x1V9eZJPo/eeMzFPxrH73malhsKidQ2NL4/bQddF31/723aI1vL9QSHvtfmOxS9r9NLK2pNrSlXDN8rKyuTUCgkvXv3bjZd769Zs6bTXmfWrFly4403dtryAAAA4ilZYig7SaD1PDTQLurmNwczeoCgBxiaqNKztzp8YnB65Bn5xp4teib6lKF95Kf/dVjTlZUOtZaOJp50GEh76/J0dPhftEPLtE205seXx1CdOvTMTewEpD3cTWtIabvpZ6QJqUMd7hbLoYEH4sbL1seinZ367Jz8jkTzWppY0gRNW0mfljXW7OXqdkgTO9oTSHuz2oJ2wsey2nwv0ayf9oTVNEu07RW5TK27FLl+kdtxr50482pP3MakjCakVHbLXrnK45FeOelmH6NDAbXYuP7udHu8LxAWn88j4S8zTvozNPmhiKe3TAl7IubzRDxuWZaErcakkv1zNicjLB0m2TiXfXVaTYQ1pqC+7Bal//N4mhJc9uuGvlwv+3PR91dqemxZZshfa1L9XtPbTYfHu2b4nhOmTZsmFRUVTX9btmyJ9yoBAAAkPCdiqMheSho4azCst5ok+OXEw01h1rZ6tpw9ukgGF+TIiP755sC7I7VY9Lm6jPYsq7N63xxsaJkeGGoR2sraQKcPPXMb/c799rShcuMZX5frT/uauZ1+2tc6VH8plkMDE6l3VrzFop2d+uyc/I5E81qDeuWYIdTt6eVpL1e3Q7npfjOs7avl64mCoJmu37m23ks063dUYZ4cVZgbdXtFLjM7zdfYi/bL9TM9phrzNuL36NC6oCkwru9NH9eC4Jr4aq1Xbsu2sH93Vlh7Qln7JVMOsIgW6//lvJHTpJGuatPjX85g97LSfaO+F02INT3X4zG9rEyvKUv7TX21bE2Y2Z+Lftb6/rS3lT5fa0i1Rntq6eN6NT4nxe10Ss+ePcXn88mOHTuaTdf7ffr06bTX0boJiVI7AUDiOLJnmqwpq49qPgBIJMkWQx2sl1IserZ09vp3dB0je43ZvV80qaAHQ3Zh3fPHDzB1TA70+KEOPXMjOwHZmcs72OfX2Z9PPHpnxVss2tmpz87J70g0rzX52AHy/Pul7erlGblcLZrt82qvmgZJ9TVesELrA2pC42DvJZr10xMO6ovydVG1V+QyN5ir5mltqaCU7wuYhI9eIEM7HOnV8bTH0GE9skzvJ11W37x0sz04UK/cyLbQ/ZT+rgLB0Je1nBoTQVYryabIVJpZZEQvKrPaETOEtdaVv7GHlya9NHnW2KOr8daywia5ZK7Upyc+6oMmWeX3WOa9p4hXQlbILEfp8zSBpfUZtUB7cY9M2VZZJ2OKu5saVVv21Ep6irfZED59Da1jNaBHlnxjUE9xUty2UqmpqTJ69GhZsGCBnHXWWWZaOBw296+44op4rRYAl/jrJd+QUbe+HtV8AJBIkjGGaitJcKhD65zUGesYTXJrUK/shE7QuVmshgbGeuhoVxOLdnbqs3PyOxLNa2lSpb1JssjlrtzceHU6rf+kV5vT5I5e0S6a9xJtW7SnvVous1tGquyVgHmsW2ZqU/JM17ViX4PUpYSalqX04gAHawv7d6eFzzNTvWZ7rx2StNa5nWNqkW9q1iOqaZqnMRFl83qk8SqIXo8EggGpawib5/XMSjPL2llZbx4f0D3LvAetC6VX39NlaIH0yDpVeqvL0cShvm/9XHbXBMz7+OHoIjn6sHxzlT1dptaQ0iF72kNKE1L6vi+ZUCL+Awzvi5W4ps71UsaTJ0+WMWPGyNixY83ljGtqasyVZNQFF1wghYWFpqaBXdjz448/bvp/aWmprFy5UrKzs2Xw4MHxfCsAupjuuRlyWPcM2byn9oDz6OM6HwAkGrfFUJ3dsyVR1/Fgya2ukKBzMyc/n3j0zkrmdnbqs3PyOxLN9uRQkmSRy9WeOJrM0OSGDhFrz3uJpi3a214t589KaxzeatcdPKxbpqkLdai9cpvVQtwXMMXETVIoYtidXe8pM9VneiNpzydN3Cm7ZpRdH9D75TC7jBRf41UCw3a9p7BJoulVABuTaWEzXdtb1+vkob1ly559smZHlUlSmcLnPq+UdM+QE4/oZXqEbauoM4lHXZ/I92G/l4fe3GCu6qrD5rWHm/aQ0oTUmSMbk3RO8lgtB2k67P777zeXKN6+fbuMHDlS7r33Xhk3bpx57IQTTpDi4mJ58sknzf1NmzbJwIED91vGhAkTZOHChVG9nl45Ji8vz9RGyM3N7eR3A6CrOf6O11tNTGlC6j//8624rBOAxJKosQMxFIDVpRVNB9J68KsHrFprh95ziJYWPCfR3b62sH93S9aXyeY9+0xPI80pacJJk0h989PlhMMLzO9Qe5P97zubZVVphRl2p6+hyaKsdL+MPqybnD6ir7mi36rSyqYrDh7VL1fGD+opffLSD5hMU+t3Vcni9bultiFoknHfHNTL9HKK5n0Eg2FzlT0taq41pHTIXmf3kIo2fop7UsppiRpYAoifPZW1ctGfl8u2qjrpm5Muj14wmh5SAJoQO9AOQCIjqQDE73enhdLX76gWvSaeliAfUpAt+VmpzRJBOu9nZdWyZnuV7KqqM1f0O7JPjpT0bLyoRrL+hklKdbBhAAAAiB2IoQAAQOxyL85WsAIAAAAAAABISgEAAAAAACAe6CkFAAAAAAAAx5GUAgAAAAAAgONISgEAAAAAAMBxJKUAAAAAAADgOJJSAAAAAAAAcBxJKQAAAAAAADiOpBQAAAAAAAAcR1IKAAAAAAAAjiMpBQAAAAAAAMf5xWUsyzK3lZWV8V4VAADQBdgxgx1DuBUxFAAA6Oz4yXVJqaqqKnPbv3//eK8KAADoYjFEXl6euBUxFAAA6Oz4yWO57LRfOByWL774QnJycsTj8cR7dRIym6kJuy1btkhubm68V8d1aH/a3q347tP+iUxDJQ2o+vXrJ16veysfxCqG4vdPe/Ad4XfDtqTzsW2lTeL9PYk2fnJdTyltjKKionivRsLTLyRJKdrfjfju0/5uxvf/wNzcQ8qpGIrvH+3Bd4TfDdsStq1OYH/jXJtEEz+593QfAAAAAAAA4oakFAAAAAAAABxHUgrNpKWlycyZM80tnEf7xw9tH1+0P+0P9+L3T3vwHeF3w7aEbSv7G/fug11X6BwAAAAAAADxR08pAAAAAAAAOI6kFAAAAAAAABxHUgoAAAAAAACOIynlMnv27JGf/vSnkpubK/n5+XLhhRdKdXV1m8+pq6uTX/ziF9KjRw/Jzs6Ws88+W3bs2NHqvLt375aioiLxeDxSXl4eo3fRdcWi/T/44AM599xzpX///pKRkSFf+9rX5J577nHg3SS+OXP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",
|
||
"text/plain": [
|
||
"<Figure size 1200x1000 with 4 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"fig, axes = plt.subplots(2, 2, figsize=(12, 10))\n",
|
||
"\n",
|
||
"for ax, col, label in zip(axes.flat, param_cols, labels):\n",
|
||
" ax.scatter(df[col], df[\"WER\"], alpha=0.6)\n",
|
||
" ax.set_xlabel(label)\n",
|
||
" ax.set_ylabel(\"WER\")\n",
|
||
" ax.set_title(f\"Effect of {label} on WER\")\n",
|
||
"\n",
|
||
"plt.tight_layout()\n",
|
||
"plt.savefig(\"hyperparameter_analysis_wer.png\", dpi=150)\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 35,
|
||
"id": "1a7e981d",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Best CER: 0.011535\n",
|
||
"Best WER: 0.098902\n",
|
||
"\n",
|
||
"Config:\n",
|
||
" textline_orientation: True\n",
|
||
" use_doc_orientation_classify: False\n",
|
||
" use_doc_unwarping: False\n",
|
||
" text_det_thresh: 0.4690\n",
|
||
" text_det_box_thresh: 0.5412\n",
|
||
" text_det_unclip_ratio: 0.0\n",
|
||
" text_rec_score_thresh: 0.6350\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"best = df.loc[df[\"CER\"].idxmin()]\n",
|
||
"print(f\"Best CER: {best['CER']:.6f}\")\n",
|
||
"print(f\"Best WER: {best['WER']:.6f}\")\n",
|
||
"print(f\"\\nConfig:\")\n",
|
||
"print(f\" textline_orientation: {best['config/textline_orientation']}\")\n",
|
||
"print(f\" use_doc_orientation_classify: {best['config/use_doc_orientation_classify']}\")\n",
|
||
"print(f\" use_doc_unwarping: {best['config/use_doc_unwarping']}\")\n",
|
||
"print(f\" text_det_thresh: {best['config/text_det_thresh']:.4f}\")\n",
|
||
"print(f\" text_det_box_thresh: {best['config/text_det_box_thresh']:.4f}\")\n",
|
||
"print(f\" text_det_unclip_ratio: {best['config/text_det_unclip_ratio']}\")\n",
|
||
"print(f\" text_rec_score_thresh: {best['config/text_rec_score_thresh']:.4f}\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "cfacaf35",
|
||
"metadata": {},
|
||
"source": [
|
||
"| Metric | Baseline | Tuned | Improvement |\n",
|
||
"|--------|----------|-------|-------------|\n",
|
||
"| CER | 0.01258 | 0.01154 | **-8.3%** |\n",
|
||
"| WER | 0.10407 | 0.09890 | **-5.0%** |"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "7070a6e6",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Análisis de Optimización de Hiperparámetros de PaddleOCR\n",
|
||
"\n",
|
||
"### Resumen del Experimento\n",
|
||
"\n",
|
||
"Se realizaron 64 pruebas de optimización de hiperparámetros utilizando Ray Tune con el algoritmo de búsqueda Optuna durante aproximadamente 3 horas y 20 minutos.\n",
|
||
"\n",
|
||
"**Configuración base evaluada:**\n",
|
||
"- Dataset: 5 documentos PDF de prueba (documentos académicos UNIR)\n",
|
||
"- Métricas objetivo: CER (Character Error Rate) y WER (Word Error Rate)\n",
|
||
"\n",
|
||
"### Resultados Principales\n",
|
||
"\n",
|
||
"| Métrica | Baseline | Optimizado | Mejora Relativa |\n",
|
||
"|---------|----------|------------|-----------------|\n",
|
||
"| CER | 1.26% | 1.15% | **8.3%** |\n",
|
||
"| WER | 10.41% | 9.89% | **5.0%** |\n",
|
||
"\n",
|
||
"### Configuración Óptima Encontrada\n",
|
||
"\n",
|
||
"| Parámetro | Valor |\n",
|
||
"|--------------------------------|---------|\n",
|
||
"| `textline_orientation` | True |\n",
|
||
"| `use_doc_orientation_classify` | False |\n",
|
||
"| `use_doc_unwarping` | False |\n",
|
||
"| `text_det_thresh` | 0.4690 |\n",
|
||
"| `text_det_box_thresh` | 0.5412 |\n",
|
||
"| `text_det_unclip_ratio` | 0.0 |\n",
|
||
"| `text_rec_score_thresh` | 0.6350 |\n",
|
||
"\n",
|
||
"### Análisis de Correlación\n",
|
||
"\n",
|
||
"| Parámetro | Correlación con CER | Correlación con WER | Interpretación |\n",
|
||
"|----------------------------|---------------------|---------------------|------------------------------------|\n",
|
||
"| `text_det_thresh` | **-0.523** | **-0.521** | Correlación negativa fuerte |\n",
|
||
"| `text_det_box_thresh` | +0.226 | +0.227 | Correlación positiva débil |\n",
|
||
"| `text_rec_score_thresh` | -0.161 | -0.173 | Correlación negativa débil |\n",
|
||
"| `text_det_unclip_ratio` | NaN | NaN | Sin varianza (fijado en 0.0) |\n",
|
||
"\n",
|
||
"### Hallazgos Clave\n",
|
||
"\n",
|
||
"#### 1. Umbral de Detección de Píxeles (`text_det_thresh`)\n",
|
||
"\n",
|
||
"Este parámetro mostró la correlación más significativa con el rendimiento del OCR:\n",
|
||
"\n",
|
||
"- **Valores muy bajos (<0.1)**: Provocan fallos catastróficos con CER del 40-50%\n",
|
||
"- **Valores medios-altos (0.3-0.6)**: Producen los mejores resultados\n",
|
||
"- **Valor óptimo**: 0.4690\n",
|
||
"\n",
|
||
"La explicación técnica es que umbrales bajos generan falsos positivos en la detección de texto, lo que corrompe el proceso de reconocimiento posterior.\n",
|
||
"\n",
|
||
"#### 2. Orientación de Línea de Texto (`textline_orientation`)\n",
|
||
"\n",
|
||
"| Configuración | CER Medio | WER Medio | Muestras |\n",
|
||
"|---------------|-----------|-----------|----------|\n",
|
||
"| True | 3.76% | 12.73% | 53 |\n",
|
||
"| False | 12.40% | 21.71% | 11 |\n",
|
||
"\n",
|
||
"Habilitar la corrección de orientación de línea de texto reduce el CER en un **69.7%** comparado con deshabilitarlo.\n",
|
||
"\n",
|
||
"#### 3. Umbral de Caja de Detección (`text_det_box_thresh`)\n",
|
||
"\n",
|
||
"- Correlación positiva débil indica que valores extremos (muy altos o muy bajos) perjudican el rendimiento\n",
|
||
"- El valor óptimo (0.5412) se encuentra en el rango medio\n",
|
||
"\n",
|
||
"#### 4. Umbral de Reconocimiento (`text_rec_score_thresh`)\n",
|
||
"\n",
|
||
"- Ligero beneficio al usar umbrales más altos\n",
|
||
"- Filtra predicciones de baja confianza, mejorando la precisión final\n",
|
||
"- Valor óptimo: 0.6350\n",
|
||
"\n",
|
||
"### Estadísticas Descriptivas del Experimento\n",
|
||
"\n",
|
||
"| Estadística | CER | WER | Tiempo (s) |\n",
|
||
"|-------------|----------|----------|------------|\n",
|
||
"| Media | 5.25% | 14.28% | 347.6 |\n",
|
||
"| Desv. Est. | 11.03% | 10.75% | 7.9 |\n",
|
||
"| Mínimo | 1.15% | 9.89% | 321.0 |\n",
|
||
"| Máximo | 51.61% | 59.45% | 368.6 |\n",
|
||
"| Mediana | 1.23% | 10.20% | 346.4 |\n",
|
||
"\n",
|
||
"### Limitaciones del Experimento\n",
|
||
"\n",
|
||
"1. **Tamaño del dataset**: Solo 5 documentos PDF de prueba\n",
|
||
"2. **Parámetro sin varianza**: `text_det_unclip_ratio` quedó fijado en 0.0 durante todo el experimento\n",
|
||
"3. **Ejecución en CPU**: Sin aceleración GPU disponible (tiempo por página ~69s)\n",
|
||
"\n",
|
||
"### Conclusiones\n",
|
||
"\n",
|
||
"1. El parámetro más crítico para optimizar es `text_det_thresh`, con una correlación de -0.52 con el CER\n",
|
||
"2. La habilitación de `textline_orientation` es esencial para documentos en español\n",
|
||
"3. La optimización de hiperparámetros logró una mejora del 8.3% en CER sobre la configuración por defecto\n",
|
||
"4. Los valores extremos en los umbrales de detección provocan degradación significativa del rendimiento"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "9a38b3c4",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Baseline vs HyperParam adjust"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 36,
|
||
"id": "6c234f69",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"C:\\Users\\sji\\AppData\\Local\\Temp\\ipykernel_47848\\3956412736.py:16: UserWarning: `lang` and `ocr_version` will be ignored when model names or model directories are not `None`.\n",
|
||
" ocr = PaddleOCR(\n",
|
||
"\u001b[32mCreating model: ('PP-LCNet_x1_0_doc_ori', None)\u001b[0m\n",
|
||
"\u001b[32mModel files already exist. Using cached files. To redownload, please delete the directory manually: `C:\\Users\\sji\\.paddlex\\official_models\\PP-LCNet_x1_0_doc_ori`.\u001b[0m\n",
|
||
"\u001b[32mCreating model: ('UVDoc', None)\u001b[0m\n",
|
||
"\u001b[32mModel files already exist. Using cached files. To redownload, please delete the directory manually: `C:\\Users\\sji\\.paddlex\\official_models\\UVDoc`.\u001b[0m\n",
|
||
"\u001b[32mCreating model: ('PP-LCNet_x1_0_textline_ori', None)\u001b[0m\n",
|
||
"\u001b[32mModel files already exist. Using cached files. To redownload, please delete the directory manually: `C:\\Users\\sji\\.paddlex\\official_models\\PP-LCNet_x1_0_textline_ori`.\u001b[0m\n",
|
||
"\u001b[32mCreating model: ('PP-OCRv5_server_det', None)\u001b[0m\n",
|
||
"\u001b[32mModel files already exist. Using cached files. To redownload, please delete the directory manually: `C:\\Users\\sji\\.paddlex\\official_models\\PP-OCRv5_server_det`.\u001b[0m\n",
|
||
"\u001b[32mCreating model: ('PP-OCRv5_server_rec', None)\u001b[0m\n",
|
||
"\u001b[32mModel files already exist. Using cached files. To redownload, please delete the directory manually: `C:\\Users\\sji\\.paddlex\\official_models\\PP-OCRv5_server_rec`.\u001b[0m\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"24 out of 24 - 77.74ss\r"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"import os\n",
|
||
"from paddleocr import PaddleOCR\n",
|
||
"from paddle_ocr_tuning import evaluate_text, assemble_from_paddle_result\n",
|
||
"from dataset_manager import ImageTextDataset\n",
|
||
"import numpy as np\n",
|
||
"import time\n",
|
||
"\n",
|
||
"PDF_FOLDER = './dataset' # Folder containing PDF files\n",
|
||
"PDF_FOLDER_ABS = os.path.abspath(PDF_FOLDER)\n",
|
||
"\n",
|
||
"dataset = ImageTextDataset(PDF_FOLDER_ABS)\n",
|
||
"\n",
|
||
"\n",
|
||
"# Initialize with better settings for Spanish/Latin text\n",
|
||
"# https://www.paddleocr.ai/main/en/version3.x/algorithm/PP-OCRv5/PP-OCRv5_multi_languages.html?utm_source=chatgpt.com#5-models-and-their-supported-languages\n",
|
||
"ocr = PaddleOCR(\n",
|
||
" text_detection_model_name=\"PP-OCRv5_server_det\",\n",
|
||
" text_recognition_model_name=\"PP-OCRv5_server_rec\",\n",
|
||
" lang=\"es\", # ignored because we are feeding directly the models\n",
|
||
")\n",
|
||
"\n",
|
||
"results = []\n",
|
||
"for i, (img, txt) in enumerate(dataset, 1): \n",
|
||
" start = time.time()\n",
|
||
" image_array = np.array(img)\n",
|
||
" out = ocr.predict(\n",
|
||
" image_array\n",
|
||
" )\n",
|
||
" out_opti = ocr.predict(\n",
|
||
" image_array,\n",
|
||
" use_doc_orientation_classify=best['config/use_doc_orientation_classify'],\n",
|
||
" use_doc_unwarping=best['config/use_doc_unwarping'],\n",
|
||
" use_textline_orientation=best['config/textline_orientation'],\n",
|
||
" text_det_thresh=best['config/text_det_thresh'],\n",
|
||
" text_det_box_thresh=best['config/text_det_box_thresh'],\n",
|
||
" text_det_unclip_ratio=best['config/text_det_unclip_ratio'],\n",
|
||
" text_rec_score_thresh=best['config/text_rec_score_thresh']\n",
|
||
" )\n",
|
||
" # ocr time and progress\n",
|
||
" elapsed = time.time() - start\n",
|
||
" print(f\"{i} out of {len(dataset)} - {elapsed:.2f}s\", end='\\r')\n",
|
||
"\n",
|
||
" #store metrics\n",
|
||
" paddle_text = assemble_from_paddle_result(out)\n",
|
||
" paddle_adjust_text = assemble_from_paddle_result(out_opti)\n",
|
||
" results.append({'Model': 'PaddleOCR', 'Prediction': paddle_text, **evaluate_text(txt, paddle_text)})\n",
|
||
" results.append({'Model': 'PaddleOCR-HyperAdjust', 'Prediction': paddle_adjust_text, **evaluate_text(txt, paddle_adjust_text)})"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 37,
|
||
"id": "e00e155d",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Benchmark results saved as ai_ocr_benchmark_finetune_results_20251208_122426.csv\n",
|
||
" WER CER\n",
|
||
"Model \n",
|
||
"PaddleOCR 0.149400 0.077756\n",
|
||
"PaddleOCR-HyperAdjust 0.076225 0.014869\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
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",
|
||
"text/plain": [
|
||
"<Figure size 800x500 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"import os\n",
|
||
"import pandas as pd\n",
|
||
"from datetime import datetime\n",
|
||
"import matplotlib.pyplot as plt\n",
|
||
"\n",
|
||
"OUTPUT_FOLDER = 'results'\n",
|
||
"os.makedirs(OUTPUT_FOLDER, exist_ok=True)\n",
|
||
"\n",
|
||
"df_results = pd.DataFrame(results)\n",
|
||
"\n",
|
||
"# Generate a unique filename with timestamp\n",
|
||
"timestamp = datetime.now().strftime(\"%Y%m%d_%H%M%S\")\n",
|
||
"filename = f\"ai_ocr_benchmark_finetune_results_{timestamp}.csv\"\n",
|
||
"filepath = os.path.join(OUTPUT_FOLDER, filename)\n",
|
||
"\n",
|
||
"df_results.to_csv(filepath, index=False)\n",
|
||
"print(f\"Benchmark results saved as {filename}\")\n",
|
||
"\n",
|
||
"# Summary by model\n",
|
||
"summary = df_results.groupby('Model')[['WER', 'CER']].mean()\n",
|
||
"print(summary)\n",
|
||
"\n",
|
||
"# Plot\n",
|
||
"summary.plot(kind='bar', figsize=(8,5), title='AI OCR Benchmark (WER & CER)')\n",
|
||
"plt.ylabel('Error Rate')\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "75a15927",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Comparación Final: PaddleOCR Baseline vs Configuración Optimizada\n",
|
||
"\n",
|
||
"### Resultados del Benchmark\n",
|
||
"\n",
|
||
"| Modelo | CER | Precisión de Caracteres |\n",
|
||
"|------------------------|--------|-------------------------|\n",
|
||
"| PaddleOCR (Baseline) | 7.78% | **92.22%** |\n",
|
||
"| PaddleOCR-HyperAdjust | 1.49% | **98.51%** |\n",
|
||
"\n",
|
||
"| Modelo | WER | Precisión de Palabras |\n",
|
||
"|------------------------|--------|-------------------------|\n",
|
||
"| PaddleOCR (Baseline) | 14.94% | **85.06%** |\n",
|
||
"| PaddleOCR-HyperAdjust | 7.62% | **92.38%** |\n",
|
||
"\n",
|
||
"### Interpretación Correcta\n",
|
||
"\n",
|
||
"#### Lo que realmente muestran los datos\n",
|
||
"\n",
|
||
"1. **El baseline ya era funcional**: Con 92.22% de precisión a nivel de carácter, el sistema base ya producía resultados utilizables.\n",
|
||
"\n",
|
||
"2. **La mejora en términos absolutos**:\n",
|
||
" - Precisión de caracteres: 92.22% → 98.51% (**+6.29 puntos porcentuales**)\n",
|
||
" - Precisión de palabras: 85.06% → 92.38% (**+7.32 puntos porcentuales**)\n",
|
||
"\n",
|
||
"3. **Reducción del error residual**:\n",
|
||
" - El CER se redujo de 7.78% a 1.49% (reducción del 80.9% del error)\n",
|
||
" - Pero en términos de precisión, pasamos de 92% a 98.5%\n",
|
||
"\n",
|
||
"### Formas de Presentar el Resultado\n",
|
||
"\n",
|
||
"| Forma de Medición | Valor |\n",
|
||
"|--------------------------------|--------------------------------|\n",
|
||
"| Mejora en precisión (absoluta) | +6.29 puntos porcentuales |\n",
|
||
"| Reducción del error (relativa) | 80.9% menos errores |\n",
|
||
"| Precisión final alcanzada | 98.51% |\n",
|
||
"\n",
|
||
"### Conclusión Equilibrada\n",
|
||
"\n",
|
||
"> La optimización de hiperparámetros mejoró la precisión de caracteres de **92.2% a 98.5%**, una ganancia de 6.3 puntos porcentuales. Aunque el baseline ya ofrecía resultados aceptables, la configuración optimizada reduce los errores residuales en un 80.9%, lo cual es relevante para aplicaciones que requieren alta fidelidad en la extracción de texto.\n",
|
||
"\n",
|
||
"### Contexto Práctico\n",
|
||
"\n",
|
||
"En un documento de 10,000 caracteres:\n",
|
||
"\n",
|
||
"| Modelo | Errores esperados |\n",
|
||
"|-----------|-------------------|\n",
|
||
"| Baseline | ~778 caracteres |\n",
|
||
"| Optimizado| ~149 caracteres |\n",
|
||
"\n",
|
||
"La diferencia de **~629 caracteres menos con errores** puede ser significativa para tareas downstream como NER o análisis semántico."
|
||
]
|
||
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|
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