diff --git a/paddle_ocr_fine_tune_unir_raytune.ipynb b/paddle_ocr_fine_tune_unir_raytune.ipynb index e7cd47b..dec1461 100644 --- a/paddle_ocr_fine_tune_unir_raytune.ipynb +++ b/paddle_ocr_fine_tune_unir_raytune.ipynb @@ -185,7 +185,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "13103c58", "metadata": {}, "outputs": [ @@ -295,7 +295,7 @@ ], "source": [ "# Install necessary packages\n", - "%pip install transformers pillow paddleocr hf_xet paddlepaddle\n", + "%pip install transformers pillow paddleocr hf_xet paddlepaddle jiwer\n", "\n", "\n", "\n", @@ -397,6 +397,74 @@ "print(SCRIPT_DIR)" ] }, + { + "cell_type": "code", + "execution_count": 93, + "id": "9c658b58", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
Paddle version: 3.2.2\n",
+       "
\n" + ], + "text/plain": [ + "Paddle version: \u001b[1;36m3.2\u001b[0m.\u001b[1;36m2\u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
GPU available: False\n",
+       "
\n" + ], + "text/plain": [ + "GPU available: \u001b[3;91mFalse\u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
GPU count: 0\n",
+       "
\n" + ], + "text/plain": [ + "GPU count: \u001b[1;36m0\u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
Current device: cpu\n",
+       "
\n" + ], + "text/plain": [ + "Current device: cpu\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "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": 41, @@ -1343,10 +1411,10 @@ "search_space = {\n", " \"textline_orientation\": tune.choice([True, False]),\n", " \"text_det_box_thresh\": tune.uniform(0.4, 0.7),\n", - " \"text_det_unclip_ratio\": tune.uniform(1.2, 2.0),\n", - " \"text_rec_score_thresh\": tune.choice([0.0, 0.2, 0.4]),\n", - " \"line_tolerance\": tune.choice([0.5, 0.6, 0.7]),\n", - " \"min_box_score\": tune.choice([0, 0.5, 0.6])\n", + " \"text_det_unclip_ratio\": tune.uniform(1.0, 2.0),\n", + " \"text_rec_score_thresh\": tune.uniform(0, 1.0),\n", + " \"line_tolerance\": tune.uniform(0, 2.0),\n", + " \"min_box_score\": tune.uniform(0, 1.0)\n", "}\n", "KEYMAP = {\n", " \"textline_orientation\": \"textline-orientation\",\n", @@ -1464,7 +1532,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 69, "id": "8df28468", "metadata": {}, "outputs": [ @@ -1486,16 +1554,16 @@ "

Tune Status

\n", " \n", "\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "
Current time:2025-12-06 21:03:49
Running for: 00:06:59.77
Memory: 7.3/15.9 GiB
Current time:2025-12-06 22:38:36
Running for: 01:41:47.58
Memory: 4.5/15.9 GiB
\n", " \n", "
\n", "
\n", "

System Info

\n", - " Using AsyncHyperBand: num_stopped=0
Bracket: Iter 64.000: None | Iter 32.000: None | Iter 16.000: None | Iter 8.000: None | Iter 4.000: None | Iter 2.000: None | Iter 1.000: -0.09990138305449689
Logical resource usage: 2.0/16 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)\n", + " Using AsyncHyperBand: num_stopped=13
Bracket: Iter 64.000: None | Iter 32.000: None | Iter 16.000: None | Iter 8.000: None | Iter 4.000: None | Iter 2.000: None | Iter 1.000: -0.11205841913079691
Logical resource usage: 1.0/16 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)\n", "
\n", " \n", " \n", @@ -1509,10 +1577,38 @@ "htextline_orientation iter total time (s) CER WER TIME\n", "\n", "\n", - "trainable_paddle_ocr_b3bdc_00002RUNNING 127.0.0.1:10864 0.6 0 0.5329721.9115 0.4False \n", - "trainable_paddle_ocr_b3bdc_00003RUNNING 127.0.0.1:11400 0.7 0.5 0.6633 1.695260.4False \n", "trainable_paddle_ocr_b3bdc_00000TERMINATED127.0.0.1:19504 0.7 0 0.5652971.282490.2True 1 399.5250.06391320.148775376.277\n", "trainable_paddle_ocr_b3bdc_00001TERMINATED127.0.0.1:18012 0.7 0 0.6107611.788240 True 1 386.4870.13589 0.304316362.611\n", + "trainable_paddle_ocr_b3bdc_00002TERMINATED127.0.0.1:10864 0.6 0 0.5329721.9115 0.4False 1 377.0080.125817 0.356445351.958\n", + "trainable_paddle_ocr_b3bdc_00003TERMINATED127.0.0.1:11400 0.7 0.5 0.6633 1.695260.4False 1 373.1720.152864 0.2617 349.995\n", + "trainable_paddle_ocr_b3bdc_00004TERMINATED127.0.0.1:16556 0.6 0 0.5050191.882710.4False 1 379.8860.127304 0.342154355.984\n", + "trainable_paddle_ocr_b3bdc_00005TERMINATED127.0.0.1:12240 0.7 0.5 0.60097 1.6219 0.4True 1 382.5450.09812260.237748360.209\n", + "trainable_paddle_ocr_b3bdc_00006TERMINATED127.0.0.1:19712 0.7 0.5 0.4545681.359530.2False 1 397.7640.06298210.151528373.757\n", + "trainable_paddle_ocr_b3bdc_00007TERMINATED127.0.0.1:17768 0.6 0.5 0.6493151.695950.2True 1 385.2290.125408 0.252182362.127\n", + "trainable_paddle_ocr_b3bdc_00008TERMINATED127.0.0.1:14292 0.6 0.5 0.5146541.822850.4False 1 380.6470.113582 0.308721356.781\n", + "trainable_paddle_ocr_b3bdc_00009TERMINATED127.0.0.1:7292 0.6 0.6 0.6190981.790970.4False 1 375.6860.141796 0.301124352.926\n", + "trainable_paddle_ocr_b3bdc_00010TERMINATED127.0.0.1:22764 0.7 0.5 0.47992 1.648770 False 1 385.4060.08279620.218071361.063\n", + "trainable_paddle_ocr_b3bdc_00011TERMINATED127.0.0.1:6256 0.7 0.6 0.6730781.631110 True 1 377.8390.190876 0.292151353.255\n", + "trainable_paddle_ocr_b3bdc_00012TERMINATED127.0.0.1:12344 0.5 0.6 0.4971961.313680.4True 1 393.1020.065148 0.162362368.717\n", + "trainable_paddle_ocr_b3bdc_00013TERMINATED127.0.0.1:15216 0.6 0.6 0.5925431.3073 0.2True 1 391.8360.06416230.155348369.637\n", + "trainable_paddle_ocr_b3bdc_00014TERMINATED127.0.0.1:22580 0.7 0 0.6694691.853260.2False 1 358.2560.207922 0.354745335.335\n", + "trainable_paddle_ocr_b3bdc_00015TERMINATED127.0.0.1:23532 0.6 0.5 0.4460471.228360 False 1 380.0530.06346270.150588359.242\n", + "trainable_paddle_ocr_b3bdc_00016TERMINATED127.0.0.1:4760 0.6 0.5 0.4646921.653760.2False 1 366.5380.07256270.212321343.623\n", + "trainable_paddle_ocr_b3bdc_00017TERMINATED127.0.0.1:10784 0.7 0.6 0.57992 1.408870 True 1 369.3780.06798260.163942347.845\n", + "trainable_paddle_ocr_b3bdc_00018TERMINATED127.0.0.1:10972 0.7 0 0.6987421.640070.2False 1 352.4810.212477 0.32237 331.44 \n", + "trainable_paddle_ocr_b3bdc_00019TERMINATED127.0.0.1:4780 0.5 0.5 0.6516561.205430 False 1 370.6790.126101 0.21466 349.24 \n", + "trainable_paddle_ocr_b3bdc_00020TERMINATED127.0.0.1:20080 0.5 0.6 0.5562851.645390 False 1 365.1020.08233360.214002343.422\n", + "trainable_paddle_ocr_b3bdc_00021TERMINATED127.0.0.1:1072 0.6 0.5 0.5566931.592570.4True 1 371.4780.08044530.205515349.989\n", + "trainable_paddle_ocr_b3bdc_00022TERMINATED127.0.0.1:19888 0.7 0 0.4255311.325310.4False 1 376.6790.06319580.14949 354.681\n", + "trainable_paddle_ocr_b3bdc_00023TERMINATED127.0.0.1:18380 0.6 0 0.49713 1.788140.2False 1 368.4290.09891760.278952346.66 \n", + "trainable_paddle_ocr_b3bdc_00024TERMINATED127.0.0.1:10164 0.5 0.5 0.4561051.928520.2False 1 362.5730.138896 0.371172340.864\n", + "trainable_paddle_ocr_b3bdc_00025TERMINATED127.0.0.1:10396 0.6 0.6 0.6772631.407550.2True 1 367.9180.185939 0.280449346.613\n", + "trainable_paddle_ocr_b3bdc_00026TERMINATED127.0.0.1:1824 0.5 0 0.6926371.203170.4True 1 369.1470.198069 0.289923347.498\n", + "trainable_paddle_ocr_b3bdc_00027TERMINATED127.0.0.1:21808 0.7 0.6 0.6211161.853430.4True 1 361.9010.156036 0.331298340.435\n", + "trainable_paddle_ocr_b3bdc_00028TERMINATED127.0.0.1:19872 0.6 0.6 0.5298331.261360 False 1 377.2970.063679 0.154287355.93 \n", + "trainable_paddle_ocr_b3bdc_00029TERMINATED127.0.0.1:2816 0.6 0.6 0.6074591.644670.4False 1 368.3460.110535 0.249928346.987\n", + "trainable_paddle_ocr_b3bdc_00030TERMINATED127.0.0.1:7328 0.7 0.6 0.6545241.434760.4True 1 367.2740.143618 0.235326345.124\n", + "trainable_paddle_ocr_b3bdc_00031TERMINATED127.0.0.1:11640 0.5 0 0.4511051.353360 True 1 373.7460.06400480.152993352.525\n", "\n", "\n", " \n", @@ -1582,6 +1678,36 @@ "\n", "trainable_paddle_ocr_b3bdc_000000.0639132 5376.277 75.14850.148775\n", "trainable_paddle_ocr_b3bdc_000010.13589 5362.611 72.40620.304316\n", + "trainable_paddle_ocr_b3bdc_000020.125817 5351.958 70.28870.356445\n", + "trainable_paddle_ocr_b3bdc_000030.152864 5349.995 69.89530.2617 \n", + "trainable_paddle_ocr_b3bdc_000040.127304 5355.984 71.08980.342154\n", + "trainable_paddle_ocr_b3bdc_000050.0981226 5360.209 71.94280.237748\n", + "trainable_paddle_ocr_b3bdc_000060.0629821 5373.757 74.64480.151528\n", + "trainable_paddle_ocr_b3bdc_000070.125408 5362.127 72.31590.252182\n", + "trainable_paddle_ocr_b3bdc_000080.113582 5356.781 71.24290.308721\n", + "trainable_paddle_ocr_b3bdc_000090.141796 5352.926 70.46940.301124\n", + "trainable_paddle_ocr_b3bdc_000100.0827962 5361.063 72.10060.218071\n", + "trainable_paddle_ocr_b3bdc_000110.190876 5353.255 70.53770.292151\n", + "trainable_paddle_ocr_b3bdc_000120.065148 5368.717 73.63290.162362\n", + "trainable_paddle_ocr_b3bdc_000130.0641623 5369.637 73.82440.155348\n", + "trainable_paddle_ocr_b3bdc_000140.207922 5335.335 66.95990.354745\n", + "trainable_paddle_ocr_b3bdc_000150.0634627 5359.242 71.73560.150588\n", + "trainable_paddle_ocr_b3bdc_000160.0725627 5343.623 68.63450.212321\n", + "trainable_paddle_ocr_b3bdc_000170.0679826 5347.845 69.47630.163942\n", + "trainable_paddle_ocr_b3bdc_000180.212477 5331.44 66.19460.32237 \n", + "trainable_paddle_ocr_b3bdc_000190.126101 5349.24 69.748 0.21466 \n", + "trainable_paddle_ocr_b3bdc_000200.0823336 5343.422 68.59030.214002\n", + "trainable_paddle_ocr_b3bdc_000210.0804453 5349.989 69.89620.205515\n", + "trainable_paddle_ocr_b3bdc_000220.0631958 5354.681 70.83880.14949 \n", + "trainable_paddle_ocr_b3bdc_000230.0989176 5346.66 69.23140.278952\n", + "trainable_paddle_ocr_b3bdc_000240.138896 5340.864 68.075 0.371172\n", + "trainable_paddle_ocr_b3bdc_000250.185939 5346.613 69.22950.280449\n", + "trainable_paddle_ocr_b3bdc_000260.198069 5347.498 69.39910.289923\n", + "trainable_paddle_ocr_b3bdc_000270.156036 5340.435 67.98820.331298\n", + "trainable_paddle_ocr_b3bdc_000280.063679 5355.93 71.08910.154287\n", + "trainable_paddle_ocr_b3bdc_000290.110535 5346.987 69.288 0.249928\n", + "trainable_paddle_ocr_b3bdc_000300.143618 5345.124 68.92890.235326\n", + "trainable_paddle_ocr_b3bdc_000310.0640048 5352.525 70.41770.152993\n", "\n", "\n", "\n", @@ -1619,11 +1745,184 @@ "2025-12-06 21:03:33,736\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00003_3_line_tolerance=0.7000,min_box_score=0.5000,text_det_box_thresh=0.6633,text_det_unclip_ratio=1.6_2025-12-06_21-03-33\n", "2025-12-06 21:03:33,737\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00003_3_line_tolerance=0.7000,min_box_score=0.5000,text_det_box_thresh=0.6633,text_det_unclip_ratio=1.6_2025-12-06_21-03-33\n", "2025-12-06 21:03:38,480\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00003_3_line_tolerance=0.7000,min_box_score=0.5000,text_det_box_thresh=0.6633,text_det_unclip_ratio=1.6_2025-12-06_21-03-33\n", - "2025-12-06 21:03:38,481\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00003_3_line_tolerance=0.7000,min_box_score=0.5000,text_det_box_thresh=0.6633,text_det_unclip_ratio=1.6_2025-12-06_21-03-33\n" + "2025-12-06 21:03:38,481\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00003_3_line_tolerance=0.7000,min_box_score=0.5000,text_det_box_thresh=0.6633,text_det_unclip_ratio=1.6_2025-12-06_21-03-33\n", + "\u001b[36m(trainable_paddle_ocr pid=10864)\u001b[0m [2025-12-06 21:03:56,519 E 10864 15180] 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=11400)\u001b[0m [2025-12-06 21:04:08,749 E 11400 18988] 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", + "2025-12-06 21:09:44,135\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00002_2_line_tolerance=0.6000,min_box_score=0,text_det_box_thresh=0.5330,text_det_unclip_ratio=1.9115,t_2025-12-06_21-03-20\n", + "2025-12-06 21:09:44,171\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00004_4_line_tolerance=0.6000,min_box_score=0,text_det_box_thresh=0.5050,text_det_unclip_ratio=1.8827,t_2025-12-06_21-09-44\n", + "2025-12-06 21:09:44,175\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00004_4_line_tolerance=0.6000,min_box_score=0,text_det_box_thresh=0.5050,text_det_unclip_ratio=1.8827,t_2025-12-06_21-09-44\n", + "2025-12-06 21:09:49,719\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00004_4_line_tolerance=0.6000,min_box_score=0,text_det_box_thresh=0.5050,text_det_unclip_ratio=1.8827,t_2025-12-06_21-09-44\n", + "2025-12-06 21:09:49,722\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_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00004_4_line_tolerance=0.6000,min_box_score=0,text_det_box_thresh=0.5050,text_det_unclip_ratio=1.8827,t_2025-12-06_21-09-44\n", + "2025-12-06 21:09:51,685\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_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00003_3_line_tolerance=0.7000,min_box_score=0.5000,text_det_box_thresh=0.6633,text_det_unclip_ratio=1.6_2025-12-06_21-03-33\n", + "2025-12-06 21:09:51,694\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00005_5_line_tolerance=0.7000,min_box_score=0.5000,text_det_box_thresh=0.6010,text_det_unclip_ratio=1.6_2025-12-06_21-09-51\n", + "2025-12-06 21:09:51,696\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00005_5_line_tolerance=0.7000,min_box_score=0.5000,text_det_box_thresh=0.6010,text_det_unclip_ratio=1.6_2025-12-06_21-09-51\n", + "2025-12-06 21:09:56,292\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00005_5_line_tolerance=0.7000,min_box_score=0.5000,text_det_box_thresh=0.6010,text_det_unclip_ratio=1.6_2025-12-06_21-09-51\n", + "2025-12-06 21:09:56,293\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00005_5_line_tolerance=0.7000,min_box_score=0.5000,text_det_box_thresh=0.6010,text_det_unclip_ratio=1.6_2025-12-06_21-09-51\n", + "\u001b[36m(trainable_paddle_ocr pid=16556)\u001b[0m [2025-12-06 21:10:19,454 E 16556 7328] 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=12240)\u001b[0m [2025-12-06 21:10:26,611 E 12240 18476] 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", + "2025-12-06 21:16:09,646\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00004_4_line_tolerance=0.6000,min_box_score=0,text_det_box_thresh=0.5050,text_det_unclip_ratio=1.8827,t_2025-12-06_21-09-44\n", + "2025-12-06 21:16:09,711\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00006_6_line_tolerance=0.7000,min_box_score=0.5000,text_det_box_thresh=0.4546,text_det_unclip_ratio=1.3_2025-12-06_21-16-09\n", + "2025-12-06 21:16:09,713\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_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00006_6_line_tolerance=0.7000,min_box_score=0.5000,text_det_box_thresh=0.4546,text_det_unclip_ratio=1.3_2025-12-06_21-16-09\n", + "2025-12-06 21:16:15,640\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00006_6_line_tolerance=0.7000,min_box_score=0.5000,text_det_box_thresh=0.4546,text_det_unclip_ratio=1.3_2025-12-06_21-16-09\n", + "2025-12-06 21:16:15,642\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_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00006_6_line_tolerance=0.7000,min_box_score=0.5000,text_det_box_thresh=0.4546,text_det_unclip_ratio=1.3_2025-12-06_21-16-09\n", + "2025-12-06 21:16:18,859\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00005_5_line_tolerance=0.7000,min_box_score=0.5000,text_det_box_thresh=0.6010,text_det_unclip_ratio=1.6_2025-12-06_21-09-51\n", + "2025-12-06 21:16:18,876\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00007_7_line_tolerance=0.6000,min_box_score=0.5000,text_det_box_thresh=0.6493,text_det_unclip_ratio=1.6_2025-12-06_21-16-18\n", + "2025-12-06 21:16:18,876\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00007_7_line_tolerance=0.6000,min_box_score=0.5000,text_det_box_thresh=0.6493,text_det_unclip_ratio=1.6_2025-12-06_21-16-18\n", + "2025-12-06 21:16:23,437\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00007_7_line_tolerance=0.6000,min_box_score=0.5000,text_det_box_thresh=0.6493,text_det_unclip_ratio=1.6_2025-12-06_21-16-18\n", + "2025-12-06 21:16:23,440\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00007_7_line_tolerance=0.6000,min_box_score=0.5000,text_det_box_thresh=0.6493,text_det_unclip_ratio=1.6_2025-12-06_21-16-18\n", + "\u001b[36m(trainable_paddle_ocr pid=19712)\u001b[0m [2025-12-06 21:16:45,168 E 19712 3960] 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=17768)\u001b[0m [2025-12-06 21:16:53,820 E 17768 20672] 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", + "2025-12-06 21:22:48,714\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_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00007_7_line_tolerance=0.6000,min_box_score=0.5000,text_det_box_thresh=0.6493,text_det_unclip_ratio=1.6_2025-12-06_21-16-18\n", + "2025-12-06 21:22:48,768\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00008_8_line_tolerance=0.6000,min_box_score=0.5000,text_det_box_thresh=0.5147,text_det_unclip_ratio=1.8_2025-12-06_21-22-48\n", + "2025-12-06 21:22:48,771\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00008_8_line_tolerance=0.6000,min_box_score=0.5000,text_det_box_thresh=0.5147,text_det_unclip_ratio=1.8_2025-12-06_21-22-48\n", + "2025-12-06 21:22:53,439\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00006_6_line_tolerance=0.7000,min_box_score=0.5000,text_det_box_thresh=0.4546,text_det_unclip_ratio=1.3_2025-12-06_21-16-09\n", + "2025-12-06 21:22:53,461\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00009_9_line_tolerance=0.6000,min_box_score=0.6000,text_det_box_thresh=0.6191,text_det_unclip_ratio=1.7_2025-12-06_21-22-53\n", + "2025-12-06 21:22:53,462\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00009_9_line_tolerance=0.6000,min_box_score=0.6000,text_det_box_thresh=0.6191,text_det_unclip_ratio=1.7_2025-12-06_21-22-53\n", + "2025-12-06 21:22:54,552\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00008_8_line_tolerance=0.6000,min_box_score=0.5000,text_det_box_thresh=0.5147,text_det_unclip_ratio=1.8_2025-12-06_21-22-48\n", + "2025-12-06 21:22:54,553\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_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00008_8_line_tolerance=0.6000,min_box_score=0.5000,text_det_box_thresh=0.5147,text_det_unclip_ratio=1.8_2025-12-06_21-22-48\n", + "2025-12-06 21:22:58,237\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00009_9_line_tolerance=0.6000,min_box_score=0.6000,text_det_box_thresh=0.6191,text_det_unclip_ratio=1.7_2025-12-06_21-22-53\n", + "2025-12-06 21:22:58,238\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_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00009_9_line_tolerance=0.6000,min_box_score=0.6000,text_det_box_thresh=0.6191,text_det_unclip_ratio=1.7_2025-12-06_21-22-53\n", + "\u001b[36m(trainable_paddle_ocr pid=14292)\u001b[0m [2025-12-06 21:23:24,260 E 14292 17720] 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", + "2025-12-06 21:29:13,968\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00009_9_line_tolerance=0.6000,min_box_score=0.6000,text_det_box_thresh=0.6191,text_det_unclip_ratio=1.7_2025-12-06_21-22-53\n", + "2025-12-06 21:29:14,001\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00010_10_line_tolerance=0.7000,min_box_score=0.5000,text_det_box_thresh=0.4799,text_det_unclip_ratio=1._2025-12-06_21-29-14\n", + "2025-12-06 21:29:14,003\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00010_10_line_tolerance=0.7000,min_box_score=0.5000,text_det_box_thresh=0.4799,text_det_unclip_ratio=1._2025-12-06_21-29-14\n", + "2025-12-06 21:29:15,230\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00008_8_line_tolerance=0.6000,min_box_score=0.5000,text_det_box_thresh=0.5147,text_det_unclip_ratio=1.8_2025-12-06_21-22-48\n", + "2025-12-06 21:29:15,252\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_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00011_11_line_tolerance=0.7000,min_box_score=0.6000,text_det_box_thresh=0.6731,text_det_unclip_ratio=1._2025-12-06_21-29-15\n", + "2025-12-06 21:29:15,253\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00011_11_line_tolerance=0.7000,min_box_score=0.6000,text_det_box_thresh=0.6731,text_det_unclip_ratio=1._2025-12-06_21-29-15\n", + "2025-12-06 21:29:19,725\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00010_10_line_tolerance=0.7000,min_box_score=0.5000,text_det_box_thresh=0.4799,text_det_unclip_ratio=1._2025-12-06_21-29-14\n", + "2025-12-06 21:29:19,725\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00010_10_line_tolerance=0.7000,min_box_score=0.5000,text_det_box_thresh=0.4799,text_det_unclip_ratio=1._2025-12-06_21-29-14\n", + "2025-12-06 21:29:19,956\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00011_11_line_tolerance=0.7000,min_box_score=0.6000,text_det_box_thresh=0.6731,text_det_unclip_ratio=1._2025-12-06_21-29-15\n", + "2025-12-06 21:29:19,958\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00011_11_line_tolerance=0.7000,min_box_score=0.6000,text_det_box_thresh=0.6731,text_det_unclip_ratio=1._2025-12-06_21-29-15\n", + "\u001b[36m(trainable_paddle_ocr pid=22764)\u001b[0m [2025-12-06 21:29:49,308 E 22764 6536] 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", + "2025-12-06 21:35:37,866\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00011_11_line_tolerance=0.7000,min_box_score=0.6000,text_det_box_thresh=0.6731,text_det_unclip_ratio=1._2025-12-06_21-29-15\n", + "2025-12-06 21:35:37,911\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_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00012_12_line_tolerance=0.5000,min_box_score=0.6000,text_det_box_thresh=0.4972,text_det_unclip_ratio=1._2025-12-06_21-35-37\n", + "2025-12-06 21:35:37,915\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00012_12_line_tolerance=0.5000,min_box_score=0.6000,text_det_box_thresh=0.4972,text_det_unclip_ratio=1._2025-12-06_21-35-37\n", + "2025-12-06 21:35:43,961\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_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00012_12_line_tolerance=0.5000,min_box_score=0.6000,text_det_box_thresh=0.4972,text_det_unclip_ratio=1._2025-12-06_21-35-37\n", + "2025-12-06 21:35:43,963\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00012_12_line_tolerance=0.5000,min_box_score=0.6000,text_det_box_thresh=0.4972,text_det_unclip_ratio=1._2025-12-06_21-35-37\n", + "2025-12-06 21:35:45,167\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00010_10_line_tolerance=0.7000,min_box_score=0.5000,text_det_box_thresh=0.4799,text_det_unclip_ratio=1._2025-12-06_21-29-14\n", + "2025-12-06 21:35:45,186\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00013_13_line_tolerance=0.6000,min_box_score=0.6000,text_det_box_thresh=0.5925,text_det_unclip_ratio=1._2025-12-06_21-35-45\n", + "2025-12-06 21:35:45,194\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00013_13_line_tolerance=0.6000,min_box_score=0.6000,text_det_box_thresh=0.5925,text_det_unclip_ratio=1._2025-12-06_21-35-45\n", + "2025-12-06 21:35:49,781\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00013_13_line_tolerance=0.6000,min_box_score=0.6000,text_det_box_thresh=0.5925,text_det_unclip_ratio=1._2025-12-06_21-35-45\n", + "2025-12-06 21:35:49,782\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00013_13_line_tolerance=0.6000,min_box_score=0.6000,text_det_box_thresh=0.5925,text_det_unclip_ratio=1._2025-12-06_21-35-45\n", + "\u001b[36m(trainable_paddle_ocr pid=12344)\u001b[0m [2025-12-06 21:36:14,512 E 12344 12400] 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=15216)\u001b[0m [2025-12-06 21:36:20,204 E 15216 20000] 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", + "2025-12-06 21:42:17,137\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_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00012_12_line_tolerance=0.5000,min_box_score=0.6000,text_det_box_thresh=0.4972,text_det_unclip_ratio=1._2025-12-06_21-35-37\n", + "2025-12-06 21:42:17,239\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_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00014_14_line_tolerance=0.7000,min_box_score=0,text_det_box_thresh=0.6695,text_det_unclip_ratio=1.8533,_2025-12-06_21-42-17\n", + "2025-12-06 21:42:17,242\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00014_14_line_tolerance=0.7000,min_box_score=0,text_det_box_thresh=0.6695,text_det_unclip_ratio=1.8533,_2025-12-06_21-42-17\n", + "2025-12-06 21:42:21,653\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00013_13_line_tolerance=0.6000,min_box_score=0.6000,text_det_box_thresh=0.5925,text_det_unclip_ratio=1._2025-12-06_21-35-45\n", + "2025-12-06 21:42:21,673\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00015_15_line_tolerance=0.6000,min_box_score=0.5000,text_det_box_thresh=0.4460,text_det_unclip_ratio=1._2025-12-06_21-42-21\n", + "2025-12-06 21:42:21,675\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_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00015_15_line_tolerance=0.6000,min_box_score=0.5000,text_det_box_thresh=0.4460,text_det_unclip_ratio=1._2025-12-06_21-42-21\n", + "2025-12-06 21:42:23,303\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00014_14_line_tolerance=0.7000,min_box_score=0,text_det_box_thresh=0.6695,text_det_unclip_ratio=1.8533,_2025-12-06_21-42-17\n", + "2025-12-06 21:42:23,303\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00014_14_line_tolerance=0.7000,min_box_score=0,text_det_box_thresh=0.6695,text_det_unclip_ratio=1.8533,_2025-12-06_21-42-17\n", + "2025-12-06 21:42:26,244\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00015_15_line_tolerance=0.6000,min_box_score=0.5000,text_det_box_thresh=0.4460,text_det_unclip_ratio=1._2025-12-06_21-42-21\n", + "2025-12-06 21:42:26,252\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_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00015_15_line_tolerance=0.6000,min_box_score=0.5000,text_det_box_thresh=0.4460,text_det_unclip_ratio=1._2025-12-06_21-42-21\n", + "\u001b[36m(trainable_paddle_ocr pid=22580)\u001b[0m [2025-12-06 21:42:53,892 E 22580 16980] 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", + "2025-12-06 21:48:21,584\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00014_14_line_tolerance=0.7000,min_box_score=0,text_det_box_thresh=0.6695,text_det_unclip_ratio=1.8533,_2025-12-06_21-42-17\n", + "2025-12-06 21:48:21,613\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_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00016_16_line_tolerance=0.6000,min_box_score=0.5000,text_det_box_thresh=0.4647,text_det_unclip_ratio=1._2025-12-06_21-48-21\n", + "2025-12-06 21:48:21,616\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00016_16_line_tolerance=0.6000,min_box_score=0.5000,text_det_box_thresh=0.4647,text_det_unclip_ratio=1._2025-12-06_21-48-21\n", + "2025-12-06 21:48:27,021\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00016_16_line_tolerance=0.6000,min_box_score=0.5000,text_det_box_thresh=0.4647,text_det_unclip_ratio=1._2025-12-06_21-48-21\n", + "2025-12-06 21:48:27,022\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00016_16_line_tolerance=0.6000,min_box_score=0.5000,text_det_box_thresh=0.4647,text_det_unclip_ratio=1._2025-12-06_21-48-21\n", + "2025-12-06 21:48:46,315\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00015_15_line_tolerance=0.6000,min_box_score=0.5000,text_det_box_thresh=0.4460,text_det_unclip_ratio=1._2025-12-06_21-42-21\n", + "2025-12-06 21:48:46,330\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00017_17_line_tolerance=0.7000,min_box_score=0.6000,text_det_box_thresh=0.5799,text_det_unclip_ratio=1._2025-12-06_21-48-46\n", + "2025-12-06 21:48:46,334\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00017_17_line_tolerance=0.7000,min_box_score=0.6000,text_det_box_thresh=0.5799,text_det_unclip_ratio=1._2025-12-06_21-48-46\n", + "2025-12-06 21:48:51,241\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00017_17_line_tolerance=0.7000,min_box_score=0.6000,text_det_box_thresh=0.5799,text_det_unclip_ratio=1._2025-12-06_21-48-46\n", + "2025-12-06 21:48:51,245\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00017_17_line_tolerance=0.7000,min_box_score=0.6000,text_det_box_thresh=0.5799,text_det_unclip_ratio=1._2025-12-06_21-48-46\n", + "\u001b[36m(trainable_paddle_ocr pid=4760)\u001b[0m [2025-12-06 21:48:56,886 E 4760 14816] 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=10784)\u001b[0m [2025-12-06 21:49:21,382 E 10784 20052] 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", + "2025-12-06 21:54:33,574\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_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00016_16_line_tolerance=0.6000,min_box_score=0.5000,text_det_box_thresh=0.4647,text_det_unclip_ratio=1._2025-12-06_21-48-21\n", + "2025-12-06 21:54:33,590\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00018_18_line_tolerance=0.7000,min_box_score=0,text_det_box_thresh=0.6987,text_det_unclip_ratio=1.6401,_2025-12-06_21-54-33\n", + "2025-12-06 21:54:33,592\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00018_18_line_tolerance=0.7000,min_box_score=0,text_det_box_thresh=0.6987,text_det_unclip_ratio=1.6401,_2025-12-06_21-54-33\n", + "2025-12-06 21:54:38,335\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_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00018_18_line_tolerance=0.7000,min_box_score=0,text_det_box_thresh=0.6987,text_det_unclip_ratio=1.6401,_2025-12-06_21-54-33\n", + "2025-12-06 21:54:38,336\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00018_18_line_tolerance=0.7000,min_box_score=0,text_det_box_thresh=0.6987,text_det_unclip_ratio=1.6401,_2025-12-06_21-54-33\n", + "2025-12-06 21:55:00,634\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00017_17_line_tolerance=0.7000,min_box_score=0.6000,text_det_box_thresh=0.5799,text_det_unclip_ratio=1._2025-12-06_21-48-46\n", + "2025-12-06 21:55:00,660\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_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00019_19_line_tolerance=0.5000,min_box_score=0.5000,text_det_box_thresh=0.6517,text_det_unclip_ratio=1._2025-12-06_21-55-00\n", + "2025-12-06 21:55:00,665\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_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00019_19_line_tolerance=0.5000,min_box_score=0.5000,text_det_box_thresh=0.6517,text_det_unclip_ratio=1._2025-12-06_21-55-00\n", + "2025-12-06 21:55:05,476\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00019_19_line_tolerance=0.5000,min_box_score=0.5000,text_det_box_thresh=0.6517,text_det_unclip_ratio=1._2025-12-06_21-55-00\n", + "2025-12-06 21:55:05,478\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00019_19_line_tolerance=0.5000,min_box_score=0.5000,text_det_box_thresh=0.6517,text_det_unclip_ratio=1._2025-12-06_21-55-00\n", + "\u001b[36m(trainable_paddle_ocr pid=10972)\u001b[0m [2025-12-06 21:55:08,599 E 10972 6384] 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=4780)\u001b[0m [2025-12-06 21:55:35,787 E 4780 4064] 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", + "2025-12-06 22:00:30,830\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_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00018_18_line_tolerance=0.7000,min_box_score=0,text_det_box_thresh=0.6987,text_det_unclip_ratio=1.6401,_2025-12-06_21-54-33\n", + "2025-12-06 22:00:30,842\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_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00020_20_line_tolerance=0.5000,min_box_score=0.6000,text_det_box_thresh=0.5563,text_det_unclip_ratio=1._2025-12-06_22-00-30\n", + "2025-12-06 22:00:30,845\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00020_20_line_tolerance=0.5000,min_box_score=0.6000,text_det_box_thresh=0.5563,text_det_unclip_ratio=1._2025-12-06_22-00-30\n", + "2025-12-06 22:00:35,845\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00020_20_line_tolerance=0.5000,min_box_score=0.6000,text_det_box_thresh=0.5563,text_det_unclip_ratio=1._2025-12-06_22-00-30\n", + "2025-12-06 22:00:35,847\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00020_20_line_tolerance=0.5000,min_box_score=0.6000,text_det_box_thresh=0.5563,text_det_unclip_ratio=1._2025-12-06_22-00-30\n", + "\u001b[36m(trainable_paddle_ocr pid=20080)\u001b[0m [2025-12-06 22:01:06,051 E 20080 21004] 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", + "2025-12-06 22:01:16,163\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00019_19_line_tolerance=0.5000,min_box_score=0.5000,text_det_box_thresh=0.6517,text_det_unclip_ratio=1._2025-12-06_21-55-00\n", + "2025-12-06 22:01:16,176\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00021_21_line_tolerance=0.6000,min_box_score=0.5000,text_det_box_thresh=0.5567,text_det_unclip_ratio=1._2025-12-06_22-01-16\n", + "2025-12-06 22:01:16,178\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00021_21_line_tolerance=0.6000,min_box_score=0.5000,text_det_box_thresh=0.5567,text_det_unclip_ratio=1._2025-12-06_22-01-16\n", + "2025-12-06 22:01:20,878\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00021_21_line_tolerance=0.6000,min_box_score=0.5000,text_det_box_thresh=0.5567,text_det_unclip_ratio=1._2025-12-06_22-01-16\n", + "2025-12-06 22:01:20,880\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00021_21_line_tolerance=0.6000,min_box_score=0.5000,text_det_box_thresh=0.5567,text_det_unclip_ratio=1._2025-12-06_22-01-16\n", + "\u001b[36m(trainable_paddle_ocr pid=1072)\u001b[0m [2025-12-06 22:01:51,143 E 1072 22252] 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", + "2025-12-06 22:06:40,951\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00020_20_line_tolerance=0.5000,min_box_score=0.6000,text_det_box_thresh=0.5563,text_det_unclip_ratio=1._2025-12-06_22-00-30\n", + "2025-12-06 22:06:40,972\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00022_22_line_tolerance=0.7000,min_box_score=0,text_det_box_thresh=0.4255,text_det_unclip_ratio=1.3253,_2025-12-06_22-06-40\n", + "2025-12-06 22:06:40,972\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00022_22_line_tolerance=0.7000,min_box_score=0,text_det_box_thresh=0.4255,text_det_unclip_ratio=1.3253,_2025-12-06_22-06-40\n", + "2025-12-06 22:06:45,826\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_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00022_22_line_tolerance=0.7000,min_box_score=0,text_det_box_thresh=0.4255,text_det_unclip_ratio=1.3253,_2025-12-06_22-06-40\n", + "2025-12-06 22:06:45,826\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_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00022_22_line_tolerance=0.7000,min_box_score=0,text_det_box_thresh=0.4255,text_det_unclip_ratio=1.3253,_2025-12-06_22-06-40\n", + "\u001b[36m(trainable_paddle_ocr pid=19888)\u001b[0m [2025-12-06 22:07:16,150 E 19888 11400] 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", + "2025-12-06 22:07:32,369\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00021_21_line_tolerance=0.6000,min_box_score=0.5000,text_det_box_thresh=0.5567,text_det_unclip_ratio=1._2025-12-06_22-01-16\n", + "2025-12-06 22:07:32,382\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_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00023_23_line_tolerance=0.6000,min_box_score=0,text_det_box_thresh=0.4971,text_det_unclip_ratio=1.7881,_2025-12-06_22-07-32\n", + "2025-12-06 22:07:32,384\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00023_23_line_tolerance=0.6000,min_box_score=0,text_det_box_thresh=0.4971,text_det_unclip_ratio=1.7881,_2025-12-06_22-07-32\n", + "2025-12-06 22:07:37,267\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00023_23_line_tolerance=0.6000,min_box_score=0,text_det_box_thresh=0.4971,text_det_unclip_ratio=1.7881,_2025-12-06_22-07-32\n", + "2025-12-06 22:07:37,269\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_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00023_23_line_tolerance=0.6000,min_box_score=0,text_det_box_thresh=0.4971,text_det_unclip_ratio=1.7881,_2025-12-06_22-07-32\n", + "\u001b[36m(trainable_paddle_ocr pid=18380)\u001b[0m [2025-12-06 22:08:07,587 E 18380 21300] 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", + "2025-12-06 22:13:02,527\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00022_22_line_tolerance=0.7000,min_box_score=0,text_det_box_thresh=0.4255,text_det_unclip_ratio=1.3253,_2025-12-06_22-06-40\n", + "2025-12-06 22:13:02,557\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00024_24_line_tolerance=0.5000,min_box_score=0.5000,text_det_box_thresh=0.4561,text_det_unclip_ratio=1._2025-12-06_22-13-02\n", + "2025-12-06 22:13:02,560\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00024_24_line_tolerance=0.5000,min_box_score=0.5000,text_det_box_thresh=0.4561,text_det_unclip_ratio=1._2025-12-06_22-13-02\n", + "2025-12-06 22:13:07,568\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00024_24_line_tolerance=0.5000,min_box_score=0.5000,text_det_box_thresh=0.4561,text_det_unclip_ratio=1._2025-12-06_22-13-02\n", + "2025-12-06 22:13:07,569\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00024_24_line_tolerance=0.5000,min_box_score=0.5000,text_det_box_thresh=0.4561,text_det_unclip_ratio=1._2025-12-06_22-13-02\n", + "\u001b[36m(trainable_paddle_ocr pid=10164)\u001b[0m [2025-12-06 22:13:37,764 E 10164 21820] 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", + "2025-12-06 22:13:45,715\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00023_23_line_tolerance=0.6000,min_box_score=0,text_det_box_thresh=0.4971,text_det_unclip_ratio=1.7881,_2025-12-06_22-07-32\n", + "2025-12-06 22:13:45,728\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_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00025_25_line_tolerance=0.6000,min_box_score=0.6000,text_det_box_thresh=0.6773,text_det_unclip_ratio=1._2025-12-06_22-13-45\n", + "2025-12-06 22:13:45,728\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_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00025_25_line_tolerance=0.6000,min_box_score=0.6000,text_det_box_thresh=0.6773,text_det_unclip_ratio=1._2025-12-06_22-13-45\n", + "2025-12-06 22:13:50,534\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00025_25_line_tolerance=0.6000,min_box_score=0.6000,text_det_box_thresh=0.6773,text_det_unclip_ratio=1._2025-12-06_22-13-45\n", + "2025-12-06 22:13:50,535\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00025_25_line_tolerance=0.6000,min_box_score=0.6000,text_det_box_thresh=0.6773,text_det_unclip_ratio=1._2025-12-06_22-13-45\n", + "\u001b[36m(trainable_paddle_ocr pid=10396)\u001b[0m [2025-12-06 22:14:21,005 E 10396 23176] 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", + "2025-12-06 22:19:10,166\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00024_24_line_tolerance=0.5000,min_box_score=0.5000,text_det_box_thresh=0.4561,text_det_unclip_ratio=1._2025-12-06_22-13-02\n", + "2025-12-06 22:19:10,172\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_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00026_26_line_tolerance=0.5000,min_box_score=0,text_det_box_thresh=0.6926,text_det_unclip_ratio=1.2032,_2025-12-06_22-19-10\n", + "2025-12-06 22:19:10,177\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00026_26_line_tolerance=0.5000,min_box_score=0,text_det_box_thresh=0.6926,text_det_unclip_ratio=1.2032,_2025-12-06_22-19-10\n", + "2025-12-06 22:19:14,972\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00026_26_line_tolerance=0.5000,min_box_score=0,text_det_box_thresh=0.6926,text_det_unclip_ratio=1.2032,_2025-12-06_22-19-10\n", + "2025-12-06 22:19:14,972\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00026_26_line_tolerance=0.5000,min_box_score=0,text_det_box_thresh=0.6926,text_det_unclip_ratio=1.2032,_2025-12-06_22-19-10\n", + "\u001b[36m(trainable_paddle_ocr pid=1824)\u001b[0m [2025-12-06 22:19:45,228 E 1824 7268] 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", + "2025-12-06 22:19:58,469\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_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00025_25_line_tolerance=0.6000,min_box_score=0.6000,text_det_box_thresh=0.6773,text_det_unclip_ratio=1._2025-12-06_22-13-45\n", + "2025-12-06 22:19:58,478\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00027_27_line_tolerance=0.7000,min_box_score=0.6000,text_det_box_thresh=0.6211,text_det_unclip_ratio=1._2025-12-06_22-19-58\n", + "2025-12-06 22:19:58,481\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00027_27_line_tolerance=0.7000,min_box_score=0.6000,text_det_box_thresh=0.6211,text_det_unclip_ratio=1._2025-12-06_22-19-58\n", + "2025-12-06 22:20:03,306\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00027_27_line_tolerance=0.7000,min_box_score=0.6000,text_det_box_thresh=0.6211,text_det_unclip_ratio=1._2025-12-06_22-19-58\n", + "2025-12-06 22:20:03,308\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00027_27_line_tolerance=0.7000,min_box_score=0.6000,text_det_box_thresh=0.6211,text_det_unclip_ratio=1._2025-12-06_22-19-58\n", + "\u001b[36m(trainable_paddle_ocr pid=21808)\u001b[0m [2025-12-06 22:20:33,554 E 21808 14068] 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", + "2025-12-06 22:25:24,131\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00026_26_line_tolerance=0.5000,min_box_score=0,text_det_box_thresh=0.6926,text_det_unclip_ratio=1.2032,_2025-12-06_22-19-10\n", + "2025-12-06 22:25:24,145\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00028_28_line_tolerance=0.6000,min_box_score=0.6000,text_det_box_thresh=0.5298,text_det_unclip_ratio=1._2025-12-06_22-25-24\n", + "2025-12-06 22:25:24,152\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00028_28_line_tolerance=0.6000,min_box_score=0.6000,text_det_box_thresh=0.5298,text_det_unclip_ratio=1._2025-12-06_22-25-24\n", + "2025-12-06 22:25:28,966\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00028_28_line_tolerance=0.6000,min_box_score=0.6000,text_det_box_thresh=0.5298,text_det_unclip_ratio=1._2025-12-06_22-25-24\n", + "2025-12-06 22:25:28,969\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_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00028_28_line_tolerance=0.6000,min_box_score=0.6000,text_det_box_thresh=0.5298,text_det_unclip_ratio=1._2025-12-06_22-25-24\n", + "\u001b[36m(trainable_paddle_ocr pid=19872)\u001b[0m [2025-12-06 22:25:59,280 E 19872 19348] 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", + "2025-12-06 22:26:05,219\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00027_27_line_tolerance=0.7000,min_box_score=0.6000,text_det_box_thresh=0.6211,text_det_unclip_ratio=1._2025-12-06_22-19-58\n", + "2025-12-06 22:26:05,241\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00029_29_line_tolerance=0.6000,min_box_score=0.6000,text_det_box_thresh=0.6075,text_det_unclip_ratio=1._2025-12-06_22-26-05\n", + "2025-12-06 22:26:05,243\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00029_29_line_tolerance=0.6000,min_box_score=0.6000,text_det_box_thresh=0.6075,text_det_unclip_ratio=1._2025-12-06_22-26-05\n", + "2025-12-06 22:26:09,991\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00029_29_line_tolerance=0.6000,min_box_score=0.6000,text_det_box_thresh=0.6075,text_det_unclip_ratio=1._2025-12-06_22-26-05\n", + "2025-12-06 22:26:09,992\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00029_29_line_tolerance=0.6000,min_box_score=0.6000,text_det_box_thresh=0.6075,text_det_unclip_ratio=1._2025-12-06_22-26-05\n", + "\u001b[36m(trainable_paddle_ocr pid=2816)\u001b[0m [2025-12-06 22:26:40,444 E 2816 12056] 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", + "2025-12-06 22:31:46,277\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00028_28_line_tolerance=0.6000,min_box_score=0.6000,text_det_box_thresh=0.5298,text_det_unclip_ratio=1._2025-12-06_22-25-24\n", + "2025-12-06 22:31:46,294\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00030_30_line_tolerance=0.7000,min_box_score=0.6000,text_det_box_thresh=0.6545,text_det_unclip_ratio=1._2025-12-06_22-31-46\n", + "2025-12-06 22:31:46,297\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_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00030_30_line_tolerance=0.7000,min_box_score=0.6000,text_det_box_thresh=0.6545,text_det_unclip_ratio=1._2025-12-06_22-31-46\n", + "2025-12-06 22:31:51,273\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_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00030_30_line_tolerance=0.7000,min_box_score=0.6000,text_det_box_thresh=0.6545,text_det_unclip_ratio=1._2025-12-06_22-31-46\n", + "2025-12-06 22:31:51,277\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00030_30_line_tolerance=0.7000,min_box_score=0.6000,text_det_box_thresh=0.6545,text_det_unclip_ratio=1._2025-12-06_22-31-46\n", + "2025-12-06 22:32:18,349\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00029_29_line_tolerance=0.6000,min_box_score=0.6000,text_det_box_thresh=0.6075,text_det_unclip_ratio=1._2025-12-06_22-26-05\n", + "2025-12-06 22:32:18,370\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00031_31_line_tolerance=0.5000,min_box_score=0,text_det_box_thresh=0.4511,text_det_unclip_ratio=1.3534,_2025-12-06_22-32-18\n", + "2025-12-06 22:32:18,374\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00031_31_line_tolerance=0.5000,min_box_score=0,text_det_box_thresh=0.4511,text_det_unclip_ratio=1.3534,_2025-12-06_22-32-18\n", + "\u001b[36m(trainable_paddle_ocr pid=7328)\u001b[0m [2025-12-06 22:32:21,245 E 7328 10556] 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", + "2025-12-06 22:32:23,134\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00031_31_line_tolerance=0.5000,min_box_score=0,text_det_box_thresh=0.4511,text_det_unclip_ratio=1.3534,_2025-12-06_22-32-18\n", + "2025-12-06 22:32:23,136\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00031_31_line_tolerance=0.5000,min_box_score=0,text_det_box_thresh=0.4511,text_det_unclip_ratio=1.3534,_2025-12-06_22-32-18\n", + "\u001b[36m(trainable_paddle_ocr pid=11640)\u001b[0m [2025-12-06 22:32:53,354 E 11640 20276] 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", + "2025-12-06 22:37:58,564\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00030_30_line_tolerance=0.7000,min_box_score=0.6000,text_det_box_thresh=0.6545,text_det_unclip_ratio=1._2025-12-06_22-31-46\n", + "2025-12-06 22:38:36,893\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00031_31_line_tolerance=0.5000,min_box_score=0,text_det_box_thresh=0.4511,text_det_unclip_ratio=1.3534,_2025-12-06_22-32-18\n", + "2025-12-06 22:38:36,952\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to 'C:/Users/Sergio/ray_results/trainable_paddle_ocr_2025-12-06_20-56-49' in 0.0464s.\n", + "2025-12-06 22:38:36,993\tINFO tune.py:1041 -- Total run time: 6107.63 seconds (6107.54 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", @@ -1639,15 +1938,13 @@ " metrics = json.loads(last)\n", " tune.report(metrics=metrics)\n", "\n", - "scheduler = ASHAScheduler(grace_period=1, reduction_factor=2)\n", - "\n", "tuner = tune.Tuner(\n", " trainable_paddle_ocr,\n", " tune_config=tune.TuneConfig(metric=\"CER\", \n", " mode=\"min\", \n", - " scheduler=scheduler, \n", - " num_samples=32, \n", - " max_concurrent_trials=2),\n", + " search_alg=OptunaSearch(),\n", + " num_samples=128, \n", + " max_concurrent_trials=4),\n", " run_config=air.RunConfig(verbose=2, log_to_file=False),\n", " param_space=search_space\n", ")\n", @@ -1658,7 +1955,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 74, "id": "710a67ce", "metadata": {}, "outputs": [], @@ -1668,18 +1965,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 75, "id": "1ab345a3", "metadata": {}, "outputs": [ { "data": { "text/html": [ - "
Guardado: raytune_paddle_subproc_results_20251206_205059.csv\n",
+       "
Guardado: raytune_paddle_subproc_results_20251207_082539.csv\n",
        "
\n"
       ],
       "text/plain": [
-       "Guardado: raytune_paddle_subproc_results_20251206_205059.csv\n"
+       "Guardado: raytune_paddle_subproc_results_20251207_082539.csv\n"
       ]
      },
      "metadata": {},
@@ -1699,7 +1996,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 76,
    "id": "3e3a34e4",
    "metadata": {},
    "outputs": [
@@ -1727,6 +2024,8 @@
        "      CER\n",
        "      WER\n",
        "      TIME\n",
+       "      PAGES\n",
+       "      TIME_PER_PAGE\n",
        "      timestamp\n",
        "      training_iteration\n",
        "      time_this_iter_s\n",
@@ -1744,9 +2043,11 @@
        "  \n",
        "    \n",
        "      count\n",
+       "      32.000000\n",
+       "      32.000000\n",
+       "      32.000000\n",
        "      32.0\n",
-       "      32.0\n",
-       "      32.0\n",
+       "      32.000000\n",
        "      3.200000e+01\n",
        "      32.0\n",
        "      32.000000\n",
@@ -1762,126 +2063,140 @@
        "    \n",
        "    \n",
        "      mean\n",
+       "      0.115214\n",
+       "      0.244518\n",
+       "      352.898464\n",
+       "      5.0\n",
+       "      70.476907\n",
+       "      1.765054e+09\n",
        "      1.0\n",
+       "      375.418998\n",
+       "      375.418998\n",
+       "      13340.500000\n",
+       "      375.418998\n",
        "      1.0\n",
-       "      0.0\n",
-       "      1.765050e+09\n",
-       "      1.0\n",
-       "      8.467219\n",
-       "      8.467219\n",
-       "      12978.500000\n",
-       "      8.467219\n",
-       "      1.0\n",
-       "      0.519336\n",
-       "      1.587766\n",
-       "      0.243750\n",
-       "      0.612500\n",
+       "      0.568591\n",
+       "      1.568133\n",
+       "      0.225000\n",
+       "      0.621875\n",
        "      0.378125\n",
        "    \n",
        "    \n",
        "      std\n",
+       "      0.047797\n",
+       "      0.071960\n",
+       "      10.463174\n",
        "      0.0\n",
+       "      2.088799\n",
+       "      1.775117e+03\n",
        "      0.0\n",
+       "      11.087853\n",
+       "      11.087853\n",
+       "      6600.340148\n",
+       "      11.087853\n",
        "      0.0\n",
-       "      6.192431e+01\n",
-       "      0.0\n",
-       "      0.149528\n",
-       "      0.149528\n",
-       "      6630.854934\n",
-       "      0.149528\n",
-       "      0.0\n",
-       "      0.081852\n",
-       "      0.214312\n",
-       "      0.174018\n",
-       "      0.090696\n",
+       "      0.084085\n",
+       "      0.234027\n",
+       "      0.166559\n",
+       "      0.075067\n",
        "      0.262414\n",
        "    \n",
        "    \n",
        "      min\n",
+       "      0.062982\n",
+       "      0.148775\n",
+       "      331.439532\n",
+       "      5.0\n",
+       "      66.194626\n",
+       "      1.765051e+09\n",
        "      1.0\n",
+       "      352.481225\n",
+       "      352.481225\n",
+       "      1072.000000\n",
+       "      352.481225\n",
        "      1.0\n",
-       "      0.0\n",
-       "      1.765050e+09\n",
-       "      1.0\n",
-       "      8.143713\n",
-       "      8.143713\n",
-       "      928.000000\n",
-       "      8.143713\n",
-       "      1.0\n",
-       "      0.405465\n",
-       "      1.223846\n",
+       "      0.425531\n",
+       "      1.203175\n",
        "      0.000000\n",
        "      0.500000\n",
        "      0.000000\n",
        "    \n",
        "    \n",
        "      25%\n",
+       "      0.067274\n",
+       "      0.163547\n",
+       "      346.648190\n",
+       "      5.0\n",
+       "      69.230920\n",
+       "      1.765053e+09\n",
        "      1.0\n",
+       "      368.239074\n",
+       "      368.239074\n",
+       "      9455.000000\n",
+       "      368.239074\n",
        "      1.0\n",
-       "      0.0\n",
-       "      1.765050e+09\n",
-       "      1.0\n",
-       "      8.376062\n",
-       "      8.376062\n",
-       "      7607.000000\n",
-       "      8.376062\n",
-       "      1.0\n",
-       "      0.450677\n",
-       "      1.414576\n",
+       "      0.497180\n",
+       "      1.346349\n",
        "      0.000000\n",
-       "      0.500000\n",
+       "      0.600000\n",
        "      0.000000\n",
        "    \n",
        "    \n",
        "      50%\n",
+       "      0.112058\n",
+       "      0.243838\n",
+       "      352.241485\n",
+       "      5.0\n",
+       "      70.353191\n",
+       "      1.765054e+09\n",
        "      1.0\n",
+       "      374.715855\n",
+       "      374.715855\n",
+       "      12292.000000\n",
+       "      374.715855\n",
        "      1.0\n",
-       "      0.0\n",
-       "      1.765050e+09\n",
-       "      1.0\n",
-       "      8.466036\n",
-       "      8.466036\n",
-       "      12822.000000\n",
-       "      8.466036\n",
-       "      1.0\n",
-       "      0.517449\n",
-       "      1.591365\n",
-       "      0.300000\n",
+       "      0.572609\n",
+       "      1.635590\n",
+       "      0.200000\n",
        "      0.600000\n",
        "      0.500000\n",
        "    \n",
        "    \n",
        "      75%\n",
+       "      0.142252\n",
+       "      0.301922\n",
+       "      359.483947\n",
+       "      5.0\n",
+       "      71.787356\n",
+       "      1.765056e+09\n",
        "      1.0\n",
+       "      381.121178\n",
+       "      381.121178\n",
+       "      19556.000000\n",
+       "      381.121178\n",
        "      1.0\n",
-       "      0.0\n",
-       "      1.765051e+09\n",
-       "      1.0\n",
-       "      8.534602\n",
-       "      8.534602\n",
-       "      18918.000000\n",
-       "      8.534602\n",
-       "      1.0\n",
-       "      0.549884\n",
-       "      1.794517\n",
+       "      0.649900\n",
+       "      1.788165\n",
        "      0.400000\n",
        "      0.700000\n",
        "      0.600000\n",
        "    \n",
        "    \n",
        "      max\n",
+       "      0.212477\n",
+       "      0.371172\n",
+       "      376.277248\n",
+       "      5.0\n",
+       "      75.148484\n",
+       "      1.765057e+09\n",
        "      1.0\n",
+       "      399.524998\n",
+       "      399.524998\n",
+       "      23532.000000\n",
+       "      399.524998\n",
        "      1.0\n",
-       "      0.0\n",
-       "      1.765051e+09\n",
-       "      1.0\n",
-       "      8.895011\n",
-       "      8.895011\n",
-       "      23328.000000\n",
-       "      8.895011\n",
-       "      1.0\n",
-       "      0.685410\n",
-       "      1.957707\n",
+       "      0.698742\n",
+       "      1.928521\n",
        "      0.400000\n",
        "      0.700000\n",
        "      0.600000\n",
@@ -1891,58 +2206,68 @@
        ""
       ],
       "text/plain": [
-       "        CER   WER  TIME     timestamp  training_iteration  time_this_iter_s  \\\n",
-       "count  32.0  32.0  32.0  3.200000e+01                32.0         32.000000   \n",
-       "mean    1.0   1.0   0.0  1.765050e+09                 1.0          8.467219   \n",
-       "std     0.0   0.0   0.0  6.192431e+01                 0.0          0.149528   \n",
-       "min     1.0   1.0   0.0  1.765050e+09                 1.0          8.143713   \n",
-       "25%     1.0   1.0   0.0  1.765050e+09                 1.0          8.376062   \n",
-       "50%     1.0   1.0   0.0  1.765050e+09                 1.0          8.466036   \n",
-       "75%     1.0   1.0   0.0  1.765051e+09                 1.0          8.534602   \n",
-       "max     1.0   1.0   0.0  1.765051e+09                 1.0          8.895011   \n",
+       "             CER        WER        TIME  PAGES  TIME_PER_PAGE     timestamp  \\\n",
+       "count  32.000000  32.000000   32.000000   32.0      32.000000  3.200000e+01   \n",
+       "mean    0.115214   0.244518  352.898464    5.0      70.476907  1.765054e+09   \n",
+       "std     0.047797   0.071960   10.463174    0.0       2.088799  1.775117e+03   \n",
+       "min     0.062982   0.148775  331.439532    5.0      66.194626  1.765051e+09   \n",
+       "25%     0.067274   0.163547  346.648190    5.0      69.230920  1.765053e+09   \n",
+       "50%     0.112058   0.243838  352.241485    5.0      70.353191  1.765054e+09   \n",
+       "75%     0.142252   0.301922  359.483947    5.0      71.787356  1.765056e+09   \n",
+       "max     0.212477   0.371172  376.277248    5.0      75.148484  1.765057e+09   \n",
        "\n",
-       "       time_total_s           pid  time_since_restore  \\\n",
-       "count     32.000000     32.000000           32.000000   \n",
-       "mean       8.467219  12978.500000            8.467219   \n",
-       "std        0.149528   6630.854934            0.149528   \n",
-       "min        8.143713    928.000000            8.143713   \n",
-       "25%        8.376062   7607.000000            8.376062   \n",
-       "50%        8.466036  12822.000000            8.466036   \n",
-       "75%        8.534602  18918.000000            8.534602   \n",
-       "max        8.895011  23328.000000            8.895011   \n",
+       "       training_iteration  time_this_iter_s  time_total_s           pid  \\\n",
+       "count                32.0         32.000000     32.000000     32.000000   \n",
+       "mean                  1.0        375.418998    375.418998  13340.500000   \n",
+       "std                   0.0         11.087853     11.087853   6600.340148   \n",
+       "min                   1.0        352.481225    352.481225   1072.000000   \n",
+       "25%                   1.0        368.239074    368.239074   9455.000000   \n",
+       "50%                   1.0        374.715855    374.715855  12292.000000   \n",
+       "75%                   1.0        381.121178    381.121178  19556.000000   \n",
+       "max                   1.0        399.524998    399.524998  23532.000000   \n",
        "\n",
-       "       iterations_since_restore  config/text_det_box_thresh  \\\n",
-       "count                      32.0                   32.000000   \n",
-       "mean                        1.0                    0.519336   \n",
-       "std                         0.0                    0.081852   \n",
-       "min                         1.0                    0.405465   \n",
-       "25%                         1.0                    0.450677   \n",
-       "50%                         1.0                    0.517449   \n",
-       "75%                         1.0                    0.549884   \n",
-       "max                         1.0                    0.685410   \n",
+       "       time_since_restore  iterations_since_restore  \\\n",
+       "count           32.000000                      32.0   \n",
+       "mean           375.418998                       1.0   \n",
+       "std             11.087853                       0.0   \n",
+       "min            352.481225                       1.0   \n",
+       "25%            368.239074                       1.0   \n",
+       "50%            374.715855                       1.0   \n",
+       "75%            381.121178                       1.0   \n",
+       "max            399.524998                       1.0   \n",
        "\n",
-       "       config/text_det_unclip_ratio  config/text_rec_score_thresh  \\\n",
-       "count                     32.000000                     32.000000   \n",
-       "mean                       1.587766                      0.243750   \n",
-       "std                        0.214312                      0.174018   \n",
-       "min                        1.223846                      0.000000   \n",
-       "25%                        1.414576                      0.000000   \n",
-       "50%                        1.591365                      0.300000   \n",
-       "75%                        1.794517                      0.400000   \n",
-       "max                        1.957707                      0.400000   \n",
+       "       config/text_det_box_thresh  config/text_det_unclip_ratio  \\\n",
+       "count                   32.000000                     32.000000   \n",
+       "mean                     0.568591                      1.568133   \n",
+       "std                      0.084085                      0.234027   \n",
+       "min                      0.425531                      1.203175   \n",
+       "25%                      0.497180                      1.346349   \n",
+       "50%                      0.572609                      1.635590   \n",
+       "75%                      0.649900                      1.788165   \n",
+       "max                      0.698742                      1.928521   \n",
        "\n",
-       "       config/line_tolerance  config/min_box_score  \n",
-       "count              32.000000             32.000000  \n",
-       "mean                0.612500              0.378125  \n",
-       "std                 0.090696              0.262414  \n",
-       "min                 0.500000              0.000000  \n",
-       "25%                 0.500000              0.000000  \n",
-       "50%                 0.600000              0.500000  \n",
-       "75%                 0.700000              0.600000  \n",
-       "max                 0.700000              0.600000  "
+       "       config/text_rec_score_thresh  config/line_tolerance  \\\n",
+       "count                     32.000000              32.000000   \n",
+       "mean                       0.225000               0.621875   \n",
+       "std                        0.166559               0.075067   \n",
+       "min                        0.000000               0.500000   \n",
+       "25%                        0.000000               0.600000   \n",
+       "50%                        0.200000               0.600000   \n",
+       "75%                        0.400000               0.700000   \n",
+       "max                        0.400000               0.700000   \n",
+       "\n",
+       "       config/min_box_score  \n",
+       "count             32.000000  \n",
+       "mean               0.378125  \n",
+       "std                0.262414  \n",
+       "min                0.000000  \n",
+       "25%                0.000000  \n",
+       "50%                0.500000  \n",
+       "75%                0.600000  \n",
+       "max                0.600000  "
       ]
      },
-     "execution_count": 68,
+     "execution_count": 76,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1953,31 +2278,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "4ce5eb6a",
+   "execution_count": 90,
+   "id": "50fa5b59",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
        "
Correlación con CER:\n",
-       " config/text_det_box_thresh     NaN\n",
-       "config/text_det_unclip_ratio   NaN\n",
-       "config/text_rec_score_thresh   NaN\n",
-       "config/line_tolerance          NaN\n",
-       "config/min_box_score           NaN\n",
-       "CER                            NaN\n",
+       " CER                             1.000000\n",
+       "config/text_det_box_thresh      0.758837\n",
+       "config/text_det_unclip_ratio    0.387201\n",
+       "config/text_rec_score_thresh    0.193323\n",
+       "config/line_tolerance           0.141715\n",
+       "config/textline_orientation     0.035649\n",
+       "config/min_box_score           -0.185718\n",
        "Name: CER, dtype: float64\n",
        "
\n"
       ],
       "text/plain": [
        "Correlación con CER:\n",
-       " config/text_det_box_thresh     NaN\n",
-       "config/text_det_unclip_ratio   NaN\n",
-       "config/text_rec_score_thresh   NaN\n",
-       "config/line_tolerance          NaN\n",
-       "config/min_box_score           NaN\n",
-       "CER                            NaN\n",
+       " CER                             \u001b[1;36m1.000000\u001b[0m\n",
+       "config/text_det_box_thresh      \u001b[1;36m0.758837\u001b[0m\n",
+       "config/text_det_unclip_ratio    \u001b[1;36m0.387201\u001b[0m\n",
+       "config/text_rec_score_thresh    \u001b[1;36m0.193323\u001b[0m\n",
+       "config/line_tolerance           \u001b[1;36m0.141715\u001b[0m\n",
+       "config/textline_orientation     \u001b[1;36m0.035649\u001b[0m\n",
+       "config/min_box_score           \u001b[1;36m-0.185718\u001b[0m\n",
        "Name: CER, dtype: float64\n"
       ]
      },
@@ -1988,23 +2315,25 @@
      "data": {
       "text/html": [
        "
Correlación con WER:\n",
-       " config/text_det_box_thresh     NaN\n",
-       "config/text_det_unclip_ratio   NaN\n",
-       "config/text_rec_score_thresh   NaN\n",
-       "config/line_tolerance          NaN\n",
-       "config/min_box_score           NaN\n",
-       "WER                            NaN\n",
+       " WER                             1.000000\n",
+       "config/text_det_unclip_ratio    0.804665\n",
+       "config/text_det_box_thresh      0.394131\n",
+       "config/text_rec_score_thresh    0.316860\n",
+       "config/line_tolerance           0.032678\n",
+       "config/textline_orientation    -0.187603\n",
+       "config/min_box_score           -0.243325\n",
        "Name: WER, dtype: float64\n",
        "
\n"
       ],
       "text/plain": [
        "Correlación con WER:\n",
-       " config/text_det_box_thresh     NaN\n",
-       "config/text_det_unclip_ratio   NaN\n",
-       "config/text_rec_score_thresh   NaN\n",
-       "config/line_tolerance          NaN\n",
-       "config/min_box_score           NaN\n",
-       "WER                            NaN\n",
+       " WER                             \u001b[1;36m1.000000\u001b[0m\n",
+       "config/text_det_unclip_ratio    \u001b[1;36m0.804665\u001b[0m\n",
+       "config/text_det_box_thresh      \u001b[1;36m0.394131\u001b[0m\n",
+       "config/text_rec_score_thresh    \u001b[1;36m0.316860\u001b[0m\n",
+       "config/line_tolerance           \u001b[1;36m0.032678\u001b[0m\n",
+       "config/textline_orientation    \u001b[1;36m-0.187603\u001b[0m\n",
+       "config/min_box_score           \u001b[1;36m-0.243325\u001b[0m\n",
        "Name: WER, dtype: float64\n"
       ]
      },
@@ -2019,6 +2348,7 @@
     "    \"config/text_rec_score_thresh\",\n",
     "    \"config/line_tolerance\",\n",
     "    \"config/min_box_score\",\n",
+    "    \"config/textline_orientation\"\n",
     "]\n",
     "# Correlación de Pearson con CER y WER\n",
     "corr_cer = df[param_cols + [\"CER\"]].corr()[\"CER\"].sort_values(ascending=False)\n",
@@ -2030,13 +2360,156 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 91,
+   "id": "9462b7a2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "
textline_orientation=True:\n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[33mtextline_orientation\u001b[0m=\u001b[3;92mTrue\u001b[0m:\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
             CER        WER\n",
+       "count  14.000000  14.000000\n",
+       "mean    0.117115   0.229452\n",
+       "std     0.051623   0.064449\n",
+       "min     0.063913   0.148775\n",
+       "25%     0.065857   0.162757\n",
+       "50%     0.111765   0.236537\n",
+       "75%     0.152931   0.287554\n",
+       "max     0.198069   0.331298\n",
+       "
\n"
+      ],
+      "text/plain": [
+       "             CER        WER\n",
+       "count  \u001b[1;36m14.000000\u001b[0m  \u001b[1;36m14.000000\u001b[0m\n",
+       "mean    \u001b[1;36m0.117115\u001b[0m   \u001b[1;36m0.229452\u001b[0m\n",
+       "std     \u001b[1;36m0.051623\u001b[0m   \u001b[1;36m0.064449\u001b[0m\n",
+       "min     \u001b[1;36m0.063913\u001b[0m   \u001b[1;36m0.148775\u001b[0m\n",
+       "\u001b[1;36m25\u001b[0m%     \u001b[1;36m0.065857\u001b[0m   \u001b[1;36m0.162757\u001b[0m\n",
+       "\u001b[1;36m50\u001b[0m%     \u001b[1;36m0.111765\u001b[0m   \u001b[1;36m0.236537\u001b[0m\n",
+       "\u001b[1;36m75\u001b[0m%     \u001b[1;36m0.152931\u001b[0m   \u001b[1;36m0.287554\u001b[0m\n",
+       "max     \u001b[1;36m0.198069\u001b[0m   \u001b[1;36m0.331298\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n",
+       "textline_orientation=False:\n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\n",
+       "\u001b[33mtextline_orientation\u001b[0m=\u001b[3;91mFalse\u001b[0m:\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
             CER        WER\n",
+       "count  18.000000  18.000000\n",
+       "mean    0.113735   0.256237\n",
+       "std     0.046073   0.077033\n",
+       "min     0.062982   0.149490\n",
+       "25%     0.075005   0.212741\n",
+       "50%     0.112058   0.255814\n",
+       "75%     0.135998   0.318958\n",
+       "max     0.212477   0.371172\n",
+       "
\n"
+      ],
+      "text/plain": [
+       "             CER        WER\n",
+       "count  \u001b[1;36m18.000000\u001b[0m  \u001b[1;36m18.000000\u001b[0m\n",
+       "mean    \u001b[1;36m0.113735\u001b[0m   \u001b[1;36m0.256237\u001b[0m\n",
+       "std     \u001b[1;36m0.046073\u001b[0m   \u001b[1;36m0.077033\u001b[0m\n",
+       "min     \u001b[1;36m0.062982\u001b[0m   \u001b[1;36m0.149490\u001b[0m\n",
+       "\u001b[1;36m25\u001b[0m%     \u001b[1;36m0.075005\u001b[0m   \u001b[1;36m0.212741\u001b[0m\n",
+       "\u001b[1;36m50\u001b[0m%     \u001b[1;36m0.112058\u001b[0m   \u001b[1;36m0.255814\u001b[0m\n",
+       "\u001b[1;36m75\u001b[0m%     \u001b[1;36m0.135998\u001b[0m   \u001b[1;36m0.318958\u001b[0m\n",
+       "max     \u001b[1;36m0.212477\u001b[0m   \u001b[1;36m0.371172\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       ""
+      ]
+     },
+     "execution_count": 91,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "
"
+      ]
+     },
+     "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",
+    "CER: Essentially identical — orientation detection doesn't help at the character level\n",
+    "WER: True is meaningfully better (~2.7 percentage points lower mean, tighter distribution)\n",
+    "\n",
+    "This makes sense: orientation detection helps keep words intact by properly aligning text boxes, which reduces word-level errors even when individual characters are recognized correctly."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
    "id": "02fc0a87",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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wdZsVLnbXu0utYtTeZP4q6/6ogYTeNeuTu7WKwKY9k7ztD4VplbYu95w5czyWW9v26TwCtQ21S7Xeues89X9vpV/z5883PXv8YQdS7vPTAFwfzRDoeWhwq9U72hOt8PFR+PhRhdP4c6yHk/I+VyrdR3744Qfz8NjitpH+bT9+wF005T8lQGFET4qFGxUqbYhWuN2DL0aPHm3qc7W4U4sx9eKjjds02teuwtrtVYtH9fkpGjzohVbvdLVLsd6NaJdknaZdM5V+XyN8fciaXoj0ZKX1y8U1rtTnaujv6nMv9Lkb+pvaFVJPcP421nSnxbJ6Mld6EdUu79oAT08i2pVe72psevLQYe1+rA1wtTuzNjDU9dTv6AGuT2W210+7mGsduVah6YVL79pDlY9a+qBd7TWg0nnddNNNptRALxzapTrQDyosD3qHq0+i1RIcfUyBNsbUdhG6TbWRpJYMvfXWW6Yti7bt0W2jz0TRC4HmmXb/127h/jqb/VHzPyMjw1THaUmUfXHS5S+tfZXua1ptp+up+6I2dLW7wesy6CMgAkX3U21ro/mjeal5p41r9bjQAE6DH318gz/03KMlS9oWSxvl6o2FNqL1p3rR13novqHHX69evUz1qOaZlgDqOVHXyw449fhROi8NTvXird3K/TnWy0K3u33eKUwfv1Hcww5LE8xzZXH0+EtJSTHnIH2umFZ56Y2JPq5Cj0U9DrVTh7e2Z6HK/6AIdTc0lNwNXj86vSzd4NWxY8essWPHWo0aNTJdqpOSkqzOnTtbTz75pOlqadMum9OmTbOaNm1q0l100UVWz549rQ0bNrjS7Nixw7r66qutSpUqmd8rrUv8oUOHrEGDBpnf1Hm2aNHCY11KWydv7O6r+tHumdolVLuv33vvvdZnn31W7Peee+4505VYl71KlSpmWf7yl79Y33//vStNdna2WQ6drvN37xJfHvlYuBu8e7d3nd8555xj1ahRwxoyZEiRbsK6rJoPhem8Nc8C2Q3el9+xu8EvXrzY63w2bdpk3XLLLdaFF15oHvWg3/39739vrVq1ytWVffTo0VarVq3M9tBHB+jfzzzzTJnX29f90dv6r1271uw/+j3t2q9dmTWNr6fQRYsWme7suq7VqlWz7rjjDmv//v1FllnXszB/fke99tprpuuz/k7FihWtmjVrWv369bPWrFnjSmPva59//rnHd+3tpv/bPvnkE6tTp05mf61Vq5Y5bt59990i6YrbFv7MQ3388cfmcQj2dm/ZsqX19NNPexxjw4cPN8eWngMK540vx7o/55zSusG7H7PF5Wtp+ePLvml3g9fzi69KWs+5c+d6XF/00Q76WATteq/LoefUL774osg1KBD5Hy5i9J9QB2EAAADliTZAAADAcQiAAACA4xAAAQAAxyEAAgAAjkMABAAAHIcACAAAOA4PQvRCH16nb6DWB1CV9W3oAACgfOmTffRBqvqQ2cIvmC2MAMgLDX70fVIAACDy7Nu3zzxNviQEQF7Yjx7XDNRHggMAgPCXl5dnCjB8eYUIAZAXdrWXBj8EQAAARBZfmq/QCBoAADgOARAAAHAcAiAAAOA4BEAAAMBxCIAAAIDjEAABAADHIQACAACOQwAEAAAchwAIAAA4TlgEQLNmzZJ69epJQkKCdOzYUbKysopNu3XrVunbt69Jr096nDFjRpE0s2fPlpYtW7qe5JyamirvvPNOkNcCAADkF1iS+c0PsmzzAfO/DoejkL8KY9GiRTJq1CiZM2eOCX40oOnevbvs3LlTqlevXiT9yZMnpUGDBnLbbbfJgw8+6HWe+gK0qVOnSuPGjc2bYV955RXp3bu3bNq0SZo3b14OawUAgPNkbDkok97aJgdzT7nG1UxMkPRezaRHSk0JJzGWRgghpEFP+/btZebMmWa4oKDAvMhs+PDhMmbMmBK/q6VAI0eONJ/SVKtWTaZNmyaDBw/26WVqiYmJkpuby7vAAADwMfgZMn+jFA4q7Ldyzb7z8qAHQf5cv0NaBXbmzBnZsGGDpKWl/bZAFSqY4czMzID8Rn5+vixcuFBOnDhhqsK8OX36tMk09w8AAPCNVnNpyY+3EhV7nE4Pp+qwkAZAR44cMQFKjRo1PMbrcHZ29lnN+6uvvpLKlStLfHy83HfffbJkyRJp1qyZ17RTpkwxEaP90RIoAADgm6w9Rz2qvQrTsEena7pwERaNoIOhSZMmsnnzZvnss89kyJAhMnDgQNm2bZvXtGPHjjXFZfZn37595b68AABEqpxjpwKaLuobQSclJUlsbKwcOnTIY7wOJycnn9W84+LipFGjRubvtm3byueffy5PPfWUPPvss0XSaimRfgAAgP+qV0kIaLqoLwHSIEWDk1WrVrnGaSNoHS6uvU5Z6Xy1rQ8AAAisDvWrmd5edoPnwnS8Ttd04SLk3eC1C7xWT7Vr1046dOhgusFrg+VBgwaZ6QMGDJDatWubdjp2w2m7Kkv/PnDggKnq0vY+domPVmn17NlTLrnkEjl27JgsWLBA1qxZI++++24I1xQAgOgUWyHGdHXXXmAa7Lg3dbaDIp2u6cJFyAOgfv36yeHDh2XChAmm4XPr1q0lIyPD1TB67969pmeY7fvvv5c2bdq4hp988knz6dKliwlyVE5OjgmcDh48aBo160MRNfjp2rVrCNYQAIDo1yOlpunqXvg5QMk8Byhy8BwgAADKRru6a28vbfCsbX602qu8Sn78uX6HvAQIAABEj9gKMZLa8EIJd1HbDR4AAKA4BEAAAMBxCIAAAIDjEAABAADHIQACAACOQwAEAAAchwAIAAA4DgEQAABwHAIgAADgOARAAADAcQiAAACA4xAAAQAAxyEAAgAAjkMABAAAHIcACAAAOA4BEAAAcBwCIAAA4DgEQAAAwHEIgAAAgOMQAAEAAMchAAIAAI5DAAQAAByHAAgAADgOARAAAHCciqFeAAAAylt+gSVZe45KzrFTUr1KgnSoX01iK8SwIRyEAAgA4CgZWw7KpLe2ycHcU65xNRMTJL1XM+mRUjOky4byQxUYAMBRwc+Q+Rs9gh+VnXvKjNfpcAYCIACAY6q9tOTH8jLNHqfTNR2iHwEQAMARtM1P4ZIfdxr26HRNh+hHAAQAcARt8BzIdIhsBEAAAEfQ3l6BTIfIFhYB0KxZs6RevXqSkJAgHTt2lKysrGLTbt26Vfr27WvSx8TEyIwZM4qkmTJlirRv316qVKki1atXlz59+sjOnTuDvBYAgHCmXd21t1dxnd11vE7XdIh+IQ+AFi1aJKNGjZL09HTZuHGjtGrVSrp37y45OTle0588eVIaNGggU6dOleTkZK9p1q5dK0OHDpVPP/1UVq5cKb/88ot069ZNTpw4EeS1AQCEK33Oj3Z1V4WDIHtYp/M8IGeIsSwrpM3dtcRHS2tmzpxphgsKCqROnToyfPhwGTNmTInf1VKgkSNHmk9JDh8+bEqCNDC6+uqrS12mvLw8SUxMlNzcXKlataqfawQACGc8Byh6+XP9DumDEM+cOSMbNmyQsWPHusZVqFBB0tLSJDMzM2C/oxmhqlXzXqx5+vRp83HPQABAdNKHHXZtlsyToB0upAHQkSNHJD8/X2rUqOExXod37NgRkN/QEiUtIbriiiskJSXFaxptMzRp0qSA/B4AIPxpNVdqwwtDvRhwchugYNO2QFu2bJGFCxcWm0ZLoLSUyP7s27evXJcRAAA4qAQoKSlJYmNj5dChQx7jdbi4Bs7+GDZsmCxfvlw+/PBDufjii4tNFx8fbz4AAMAZQloCFBcXJ23btpVVq1Z5VFnpcGpqapnnq+26NfhZsmSJrF69WurXrx+gJQYAANEg5G+D1y7wAwcOlHbt2kmHDh3Mc320u/qgQYPM9AEDBkjt2rVNOx274fS2bdtcfx84cEA2b94slStXlkaNGrmqvRYsWCDLli0zzwLKzs4247VleKVKlUK2rgAAIDyEvBu80i7w06ZNM4FK69at5V//+pfpHq+uueYa09197ty5Zvi7777zWqLTpUsXWbNmjflbH5Dozcsvvyx33313qctDN3gAACKPP9fvsAiAwg0BEAAA0X39jvpeYAAAAIURAAEAAMchAAIAAI5DAAQAABwn5N3gAQBA6OQXWI58LxoBEAAADpWx5aBMemubHMw95RpXMzFB0ns1My+NjWZUgQEA4NDgZ8j8jR7Bj8rOPWXG6/RoRgAEAIADq70mvbVNvD0I0B6n0zVdtCIAAgDAYbL2HC1S8uNOwx6drumiFQEQAAAOk3PsVEDTRSICIAAAHKZ6lYSApotEBEAAADhMh/rVTG+v4jq763idrumiFQEQAAAOE1shxnR1V4WDIHtYp0fz84AIgAAAcKAeKTVl9p2XS3KiZzWXDuv4aH8OEA9CBADAoXqk1JSuzZJ5EjQAAHCW2AoxktrwQnEaqsAAAIDjEAABAADHIQACAACOQwAEAAAchwAIAAA4DgEQAABwHAIgAADgOARAAADAcQiAAACA4xAAAQAAxyEAAgAAjkMABAAAHIcACAAAOA4BEAAAcBwCIAAA4DghD4BmzZol9erVk4SEBOnYsaNkZWUVm3br1q3St29fkz4mJkZmzJhRJM2HH34ovXr1klq1apk0S5cuDfIaAACASBPSAGjRokUyatQoSU9Pl40bN0qrVq2ke/fukpOT4zX9yZMnpUGDBjJ16lRJTk72mubEiRNmPhpYAQAAeBNjWZYlIaIlPu3bt5eZM2ea4YKCAqlTp44MHz5cxowZU+J3tRRo5MiR5lMcLQFasmSJ9OnTx6/lysvLk8TERMnNzZWqVav69V0AABAa/ly/Q1YCdObMGdmwYYOkpaX9tjAVKpjhzMzMUC0WAABwgIqh+uEjR45Ifn6+1KhRw2O8Du/YsaNcl+X06dPm4x5BAgCA6BXyRtDhYMqUKabIzP5oNRwAAIheIQuAkpKSJDY2Vg4dOuQxXoeLa+AcLGPHjjX1hfZn37595fr7AADAIQFQXFyctG3bVlatWuUap42gdTg1NbVclyU+Pt40lnL/AACA6BWyNkBKu8APHDhQ2rVrJx06dDDP9dFu7IMGDTLTBwwYILVr1zZVVHbD6W3btrn+PnDggGzevFkqV64sjRo1MuOPHz8uu3fvdv3Gnj17TJpq1arJJZdcEpL1BAAA4SWk3eCVdoGfNm2aZGdnS+vWreVf//qX6R6vrrnmGtPdfe7cuWb4u+++k/r16xeZR5cuXWTNmjXmb/3/2muvLZJGAy17PqWhGzwAAJHHn+t3yAOgcEQABABA5ImI5wABAACECgEQAABwHAIgAADgOARAAADAcQiAAACA4xAAAQAAxyEAAgAAjkMABAAAHIcACAAAOA4BEAAAcBwCIAAA4DgEQAAAwHEIgAAAgOMQAAEAAMchAAIAAI5DAAQAAByHAAgAADgOARAAAHAcAiAAAOA4FUO9AAAARLP8Akuy9hyVnGOnpHqVBOlQv5rEVogJ9WI5HgEQAABBkrHloEx6a5sczD3lGlczMUHSezWTHik1yfcQogoMAIAgBT9D5m/0CH5Udu4pM16nI3QIgAAACEK1l5b8WF6m2eN0uqZDaBAAAQAQYNrmp3DJjzsNe3S6pkNoEAABABBg2uDZF9l5vqVD4BEAAQAQYNrbyxeTl2+lLVCIEAABZ0nr8DO/+UGWbT5g/qdOH4B2ddfeXqV1dj964hcaRIcI3eCBs0AXVwDe6HN+tKu79vbSIKi0ps7aILprs2SeD1SOKAECyogurgBKos/5mX3n5XLBeXElpqNBdGgQAAFlQBdXAL4GQY/ccFlAG04jMAiAgDKgiysAXyUnVgpow2kEBgEQUAa+3qlxRwegtAbROl6nazo4LACaNWuW1KtXTxISEqRjx46SlZVVbNqtW7dK3759TfqYmBiZMWPGWc8T8Jevd2rc0QGwG0SrwkGQPazTeUGqwwKgRYsWyahRoyQ9PV02btworVq1ku7du0tOTo7X9CdPnpQGDRrI1KlTJTk5OSDzBPzFHR2AsjSITk70vHnSYR3Pi1HLX4xlWSF9EYmWzrRv315mzpxphgsKCqROnToyfPhwGTNmTInf1RKekSNHmk+g5qny8vIkMTFRcnNzpWrVqme1foj+XmDK8nJHx0kNgLcOFNqGUKvHtYRYb6Yo+Qkcf67fIS0BOnPmjGzYsEHS0tJ+W6AKFcxwZmZmuc3z9OnTJtPcP0BpuKMD4C8NdlIbXii9W9c2/xP8OPRBiEeOHJH8/HypUaOGx3gd3rFjR7nNc8qUKTJp0qQy/R6cTYMgfXgZd3QAEFl4ErSIjB071rQZsmkJkFaZAf7c0QEAIkdIA6CkpCSJjY2VQ4cOeYzX4eIaOAdjnvHx8eYDAACcIaRtgOLi4qRt27ayatUq1zhtsKzDqampYTNPAAAQXUJeBaZVTwMHDpR27dpJhw4dzHN9Tpw4IYMGDTLTBwwYILVr1zbtdOxGztu2bXP9feDAAdm8ebNUrlxZGjVq5NM8AQCAs4U8AOrXr58cPnxYJkyYINnZ2dK6dWvJyMhwNWLeu3ev6cVl+/7776VNmzau4SeffNJ8unTpImvWrPFpngAAwNlC/hygcMRzgAAAiO7rd8hLgAAAQOjkO/ThjARAAAA4+In2k97aJgdzf3vBs76YVd9NFu2v5wj5u8AAAEDoXudz0C34Udm5p8x4nR7NCIAAAHBgtdekt7Z5vMfQZo/T6ZrubH4j85sfZNnmA+b/s5lXMFAFBgCAw2TtOVqk5Medhio6XdOV5Un3kVC1RgkQAAAOk3PsVEDTRWLVGgEQAAAOU71KQkDTlWfVWqAQAAEA4DAd6lczVVLFdXbX8Tpd0wWrai3UCIAAAHCY2Aoxpj2OKhwE2cM63d/nAQWzai3QCIAAAHCgHik1Zfadl0tyomc1lw7r+LI0Vg5W1Vow0AsMAACH6pFSU7o2Sw7Yk6DtqjVt8OytlU/M/w+w/K1aCwYCIAAAHCy2QkyZuroXNy+tOtPeXhrsWAGqWgsGqsAAAEBYV60FAyVAAAAgrKvWgoEACAAAhHXVWjBQBQYAAByHAAgAADgOARAAAHAcAiAAAOA4BEAAAMBxCIAAAIDj0A0eAACUm/wCKyyeDxTQAOjUqVMyc+ZMeeihhwI5WwAAEAUythyUSW9tk4O5v70NXt8dpq/HKO8nRPtdBXb48GFZvny5vPfee5Kfn2/G/fLLL/LUU09JvXr1ZOrUqcFYTgAAEOHBz5D5Gz2CH6UvTtXxOj1sA6CPP/5YGjduLDfddJP07NlTOnfuLNu2bZPmzZvLs88+KxMnTpR9+/YFb2kBAEBEVntNemub1zfE2+N0uqYLywBo/Pjxcv3118uXX34po0aNks8//1xuvvlmefzxx00gdN9990mlSpWCt7QAACDiZO05WqTkx52GPTpd04VlAPTVV1+ZICglJUUeffRRiYmJkb///e9y6623Bm8JAQBARMs5diqg6co9APrxxx8lKSnJ/K0lPeeee64JhgAAAIqjvb0CmS4kvcC0qis7O9v8bVmW7Ny5U06cOOGRpmXLloFbQgAAENE61K9mentpg2dvrXy0E3xy4v91iS8vMZZGMT6qUKGCqfby9hV7vP5v9w6LVHl5eZKYmCi5ublStWrVUC8OAABR0wtMuUcR9hOAZt95+Vl3hffn+u1XCdCePXvOasEAAIAz9UipaYKcws8BSg7Rc4D8KgFyCkqAAACIvCdB+3P99qsRtPb4+vnnn13Dn3zyiZw+fdo1fOzYMbn//vv9XuBZs2aZhygmJCRIx44dJSsrq8T0ixcvlqZNm5r0LVq0kBUrVnhMP3TokNx9991Sq1Yt01C7R48e8vXXX/u9XAAAILA02ElteKH0bl3b/B+K12D4HQCNHTvWBDk2fRjigQMHXMMnT540D0T0x6JFi8wzhdLT02Xjxo3SqlUr6d69u+Tk5HhNv27dOunfv78MHjxYNm3aJH369DGfLVu2mOlaoKXD3377rSxbtsykqVu3rqSlpRVprA0AAJzJ70bQ2gOsevXqZrhKlSryxRdfSIMGDVwlL1rq4k8jaC3xad++vXmHmCooKJA6derI8OHDZcyYMUXS9+vXzwQy+joOW6dOnaR169YyZ84c2bVrlzRp0sQERPqEanueycnJ5oGN99xzT6nLRBUYAACRJ2hVYIF25swZ2bBhgymdcS1QhQpmODMz0+t3dLx7eqUlRnZ6u0pOq8fc5xkfH29e5eGNfkczzf0DAACiV0gDoCNHjpjSoho1aniM12H7WUOF6fiS0mvboEsuucRU1+mDGzXIeuKJJ2T//v1y8KD3F61NmTLFRIz2R0ugAABA9PL7QYgvvPCCVK5c2fz966+/yty5c11Ph3ZvHxQq55xzjrzxxhumjVC1atUkNjbWlBhpe6Xiavs0WNJ2SDYtASIIAgAgevkVAGnJyvPPP+8a1nY1r776apE0vtLASQMUbTvkTod13t7o+NLSt23bVjZv3mzqALUE6KKLLjJtjdq1a+d1nlo9ph8AAOAMfgVA3333XUB/PC4uzgQrq1atMj237AbLOjxs2DCv30lNTTXTR44c6Rq3cuVKM74wrc5S2gV+/fr1Mnny5IAuPwAAcEAboNWrV0uzZs28NhLW0hbtdfXRRx/5tQBa9aSlSq+88ops375dhgwZYnp5DRo0yEwfMGCAqaKyjRgxQjIyMmT69OmyY8cOmThxoglu3AMmfU7QmjVrXF3hu3btagKsbt26+bVsAAAgOvlVAjRjxgy59957vXYt09KWP/3pT/KPf/xDrrrqKp/nqd3aDx8+LBMmTDANmbU7uwY4dkPnvXv3ml5cts6dO8uCBQtk/PjxMm7cOGncuLEsXbrU46302thZAyutGqtZs6YJoh555BF/VhUAAEQxv54DpA8U1ODksssu8zpdS2S0lEWDlkjGc4AAAIg8QXsZqpaoaC+rYmdWsaIpzQEAIJzeEQWcVQBUu3Zt84TlRo0aeZ3+5ZdfmionAAD8kbHlYJG3hNcM0VvC4Qx+NYK+/vrrTVuaU6d+20Ft+pJUfZ/XjTfeGMjlAwA4IPgZMn+jR/CjsnNPmfE6HQhpGyCtArv88svNs3u015W+c8tu+6NvdNenOusLTQs/qTnS0AYIAMqv2uvKJ1YXCX5sWgGWnJggHz/8O6rDELo2QBrY6NvYtau6dk23Y6eYmBjzPi4NgiI9+AEAlB9t81Nc8KP0KqPTNV1qwwvZNAjdqzC0J9iKFSvMe7Z2795tgiDtin7BBRcEbqkAAI6gDZ4DmQ4IWgBk04Cnffv2Zf06AACmt1cg0wER8TZ4AICzaVd37e1VXGd3Ha/TNR0QSARAAICQ0ef8aFd3VTgIsod1Os8DQqARAAEAQkqf8zP7zstNby93OqzjeQ4QwqoNEAAAgaJBTtdmyTwJGuWGAAgAEBa0mouu7igvVIEBAADHIQACAACOQwAEAAAchwAIAAA4DgEQAABwHAIgAADgOARAAADAcQiAAACA4xAAAQAAxyEAAgAAjkMABAAAHIcACAAAOA4BEAAAcBwCIAAA4DgEQAAAwHEIgAAAgOMQAAEAAMchAAIAAI5DAAQAABwnLAKgWbNmSb169SQhIUE6duwoWVlZJaZfvHixNG3a1KRv0aKFrFixwmP68ePHZdiwYXLxxRdLpUqVpFmzZjJnzpwgrwUAAIgUIQ+AFi1aJKNGjZL09HTZuHGjtGrVSrp37y45OTle069bt0769+8vgwcPlk2bNkmfPn3MZ8uWLa40Or+MjAyZP3++bN++XUaOHGkCojfffLMc1wwAAISrGMuyrFAugJb4tG/fXmbOnGmGCwoKpE6dOjJ8+HAZM2ZMkfT9+vWTEydOyPLly13jOnXqJK1bt3aV8qSkpJh0jzzyiCtN27ZtpWfPnvLYY4+Vukx5eXmSmJgoubm5UrVq1QCtKQAACCZ/rt8hLQE6c+aMbNiwQdLS0n5boAoVzHBmZqbX7+h49/RKS4zc03fu3NmU9hw4cEA0vvvggw9k165d0q1btyCuDQAAiBQVQ/njR44ckfz8fKlRo4bHeB3esWOH1+9kZ2d7Ta/jbU8//bT88Y9/NG2AKlasaIKq559/Xq6++mqv8zx9+rT5uEeQAAAgeoW8DVAwaAD06aefmlIgLWGaPn26DB06VN5//32v6adMmWKKzOyPVsEBAIDoFdISoKSkJImNjZVDhw55jNfh5ORkr9/R8SWl//nnn2XcuHGyZMkSueGGG8y4li1byubNm+XJJ58sUn2mxo4daxpOu5cAEQQBABC9QloCFBcXZxonr1q1yjVOG0HrcGpqqtfv6Hj39GrlypWu9L/88ov5aLWXOw20dN7exMfHm8ZS7h8AABC9QloCpLTkZeDAgdKuXTvp0KGDzJgxw/TyGjRokJk+YMAAqV27tqmmUiNGjJAuXbqYai0t4Vm4cKGsX79ennvuOTNdgxedPnr0aPMMoLp168ratWtl3rx58o9//COk6woAAMJDyAMg7a5++PBhmTBhgmnIrN3Z9Rk+dkPnvXv3epTmaA+vBQsWyPjx401VV+PGjWXp0qWm67tNgyKt1rrjjjvk6NGjJgj629/+Jvfdd19I1hEAAISXkD8HKBzxHCAAACJPxDwHCAAAIBQIgAAAgOMQAAEAAMchAAIAAI5DAAQAAByHAAgAADgOARAAAHAcAiAAAOA4BEAAAMBxCIAAAIDjEAABAADHIQACAACOQwAEAAAchwAIAAA4DgEQAABwHAIgAADgOARAAADAcQiAAACA4xAAAQAAxyEAAgAAjkMABAAAHIcACAAAOA4BEAAAcBwCIAAA4DgEQAAAwHEIgAAAgOMQAAEAAMchAAIAAI5DAAQAAByHAAgAADgOARAAAHAcAiAAAOA4YREAzZo1S+rVqycJCQnSsWNHycrKKjH94sWLpWnTpiZ9ixYtZMWKFR7TY2JivH6mTZsW5DUBAACRIOQB0KJFi2TUqFGSnp4uGzdulFatWkn37t0lJyfHa/p169ZJ//79ZfDgwbJp0ybp06eP+WzZssWV5uDBgx6fl156yQRAffv2Lcc1A5wjv8CSzG9+kGWbD5j/dRgAwlmMZVkhPVNpiU/79u1l5syZZrigoEDq1Kkjw4cPlzFjxhRJ369fPzlx4oQsX77cNa5Tp07SunVrmTNnjtff0ADp2LFjsmrVKp+WKS8vTxITEyU3N1eqVq1a5nUDnCBjy0GZ9NY2OZh7yjWuZmKCpPdqJj1SaoZ02QA4S54f1++QlgCdOXNGNmzYIGlpab8tUIUKZjgzM9Prd3S8e3qlJUbFpT906JC8/fbbpsSoOKdPnzaZ5v4B4FvwM2T+Ro/gR2XnnjLjdToAhKOQBkBHjhyR/Px8qVGjhsd4Hc7Ozvb6HR3vT/pXXnlFqlSpIrfcckuxyzFlyhQTMdofLYECUDKt5tKSH29FyPY4nU51GIBwFPI2QMGm7X/uuOMO02C6OGPHjjXFZfZn37595bqMQCTK2nO0SMlP4SBIp2s6AAg3FUP540lJSRIbG2uqqdzpcHJystfv6Hhf03/00Ueyc+dO09C6JPHx8eYDwHc5x04FNB0AOKYEKC4uTtq2bevROFkbQetwamqq1+/o+MKNmVeuXOk1/Ysvvmjmrz3LAARW9SoJAU0HAI6qAtMu8M8//7xpq7N9+3YZMmSI6eU1aNAgM33AgAGmiso2YsQIycjIkOnTp8uOHTtk4sSJsn79ehk2bJjHfLUhsz4v6J577in3dQKcoEP9aqa3V0wx03W8Ttd0ABBuQloFZndrP3z4sEyYMME0ZNbu7Brg2A2d9+7da3qG2Tp37iwLFiyQ8ePHy7hx46Rx48aydOlSSUlJ8ZjvwoULRXv46zODAARebIUY09Vde3tpsOPeGNoOinS6pgOAcBPy5wCFI54DBPiO5wABiMTrd8hLgABENn3YYddmyaa3lzZ41jY/Wu1FyQ+AcEYABOCsabCT2vBCchJAxAh5I2gAAIDyRgAEAAAchyowwE/6agfauwBlxzGEcEAABPiBHk/A2eEYQrigCgzwEW8+B84OxxDCCQEQ4APefA6cHY4hhBsCIMAHvPkcODscQwg3BECAD3jzOXB2OIYQbgiAAB/w5nPg7HAMIdwQAAE+4M3nwNnhGEK4IQAC/HjzuSr8bnPefA5wDCHyEAABfrz0c/adl0tyYoLHeB3W8TodAMcQIkOMZVlWqBci3OTl5UliYqLk5uZK1apVQ704CDM8xRbgGELkX795EjTgJ958DpwdjiGEA6rAAACA4xAAAQAAxyEAAgAAjkMABAAAHIcACAAAOA4BEAAAcBwCIAAA4DgEQAAAwHEIgAAAgOMQAAEAAMchAAIAAI5DAAQAAByHAAgAADgOb4MHfJBfYEnWnqOSc+yUVK+SIB3qVzNvtAbA8YHIRAAElCJjy0GZ9NY2OZh7yjWuZmKCpPdqJj1SapJ/cDSOD0SqsKgCmzVrltSrV08SEhKkY8eOkpWVVWL6xYsXS9OmTU36Fi1ayIoVK4qk2b59u9x0002SmJgo5513nrRv31727t0bxLVAtJ7ch8zf6BH8qOzcU2a8TgeciuMDkSzkAdCiRYtk1KhRkp6eLhs3bpRWrVpJ9+7dJScnx2v6devWSf/+/WXw4MGyadMm6dOnj/ls2bLFleabb76RK6+80gRJa9askS+//FIeeeQREzAB/lR7acmP5WWaPU6nazr4lp+Z3/wgyzYfMP+Tb5GN4wORLsayrJCevbXER0tnZs6caYYLCgqkTp06Mnz4cBkzZkyR9P369ZMTJ07I8uXLXeM6deokrVu3ljlz5pjh22+/Xc455xx59dVXy7RMeXl5puQoNzdXqlatWuZ1Q2TTi3T/5z8tNd1/7u0kqQ0vLJdlilRUk0Qfjg+EI3+u3yEtATpz5oxs2LBB0tLSflugChXMcGZmptfv6Hj39EpLjOz0GkC9/fbbcumll5rx1atXN0HW0qVLg7w2iDba4DmQ6ZyKapLoxPGBSBfSAOjIkSOSn58vNWrU8Bivw9nZ2V6/o+NLSq9VZ8ePH5epU6dKjx495L333pObb75ZbrnlFlm7dq3XeZ4+fdpEje4fQHt7BTKdE1FNEr04PhDpQt4GKNC0BEj17t1bHnzwQVM1plVpN954o6uKrLApU6aYIjP7o1VwgHZ1195exXV21/E6XdPBO310QOEG5O60/l2nazpEFo4PRLqQBkBJSUkSGxsrhw4d8hivw8nJyV6/o+NLSq/zrFixojRr1swjzWWXXVZsL7CxY8ea+kL7s2/fPglXNCQtP/qcH+3qrgoHQfawTud5QMWjmiR6cXwg0oU0AIqLi5O2bdvKqlWrPEpwdDg1NdXrd3S8e3q1cuVKV3qdpzaq3rlzp0eaXbt2Sd26db3OMz4+3jSWcv+Ea1uKK59YbRrmjli42fyvw3TFDh59zs/sOy+X5ETPai4d1vE8B6hkVJNEN44PRLKQPwhRu8APHDhQ2rVrJx06dJAZM2aYXl6DBg0y0wcMGCC1a9c21VRqxIgR0qVLF5k+fbrccMMNsnDhQlm/fr0899xzrnmOHj3a9Ba7+uqr5dprr5WMjAx56623TJf4SG9IWrjLnv08Gi7GwT3Jd22WzJOgz6KaRPdTb91NY/5/MEk1YuTi+ECkCnkApIHK4cOHZcKECaYhs7bZ0YDFbuis1VbaM8zWuXNnWbBggYwfP17GjRsnjRs3Nj28UlJSXGm00bO299Gg6YEHHpAmTZrI66+/bp4NFI0NSfUiotP1Ik11THBovtLVvWz5ptWEGqTrfuq+D1ONGD04PhCJQv4coHAUbs8B4nkbiPR3noXjc4B4vxvg7Ot3yEuAUDoakqI8BDNICbdqknAMyACULwKgCEBDUufdxZf3epVHG7NwqSahPR0ARQAUAWhI6qy7+PJeLye1MXPSugJw2IMQoxHP23DO6xVCsV5OelhhpKwrz/sCgo8SoAh73kbhkoHkKCjx8Fe03sWHar2c1MYsEtY1Wks2gXBDABRBbTzCrSFpJNzFh0Obk3BfLye1MQv3daV9ElB+CIAi7A4vXBqShlIk3MVH0no5qY1ZOK9rtJZsAuGKNkDlLFrbrpSncL+Lj7T1clIbs3Be10hpnwRECwKgMLrDUzpd08F5b6EO5Xo56Z1O4bqu0Vqy6a1R95lfCzyGI+mcRwP16EEVWDmK1rYr5S1aX68Q6vVyUhuzcFzXaC3Z9Fblr9nsHvNESiNvGqhHF0qAylE03+GVt3C9i4/09bLbmPVuXdv8H43BT7iuazSWbBZX5V+4wCcSmgDQfCH6UAJUjpIqx0flHV6ohONdfCBE63ohvEsAy7PKv7hG3n9dskV+PpMvyYmVwmqfp4F6dCIAKse7h4lvbi0xTTT1tikv0dorLlrXC8553ldpVf7egqAfTpyRB//3i7CrFqP5QnQiAArhsz0kwu/wAARetJQAnm1VfiDfQ3e2aL4QnQiAwqQYOBLv8BA40fpSVycIxrYLRQlgoNfjbKvyw+nZR9HaQN3pCIDCpBj4yVtbyRWNk4K9OAhD9CyJXNGy7YKxHqU9dNIX4dIzNpwfoImyoxdYmBSdHjlxOtiLgjBEz5LIFS3bLljrUdJDJ/0V6p6x4fwATZQdAVCQUXSK4vBgzMgVLdsu2OtR3GMd/I0TwqFqKdSPqEDgUQUWZBSdojj0LIlc0bLtymM9vDXqblv3Atnw3x8lO++UTF6+VY6e+MXrd8OtailaGqjj/xAABVm0PdsDgUPPksgVLduuvNbDW6Nue7jSORXM+VEi5PzIIyqiB1Vg5YCiU3hD9WjkipZtFw7rwfkRoUIJUDmh6BSFUT0auaJl24XLenB+RChQAuTgdw8htOhZErmiZduF03pwfkR5IwACQoji/8gVLdsuWtYD8FeMZVnh3U8zBPLy8iQxMVFyc3OlatWqoV4cOABPgo5c0bLtomU94Gx5fly/aQMEhAF6lkSuaNl20bIegK+oAgMAAI5DCRDgYFR7AHAqAiDAoaLlRZ4AUBZUgQEOFC0v8gSAsiIAAhwmWl7kCQBngwAIcBh/XoAJANEqLAKgWbNmSb169SQhIUE6duwoWVlZJaZfvHixNG3a1KRv0aKFrFixwmP63XffLTExMR6fHj16BHktgMgQLS/yBICIDoAWLVoko0aNkvT0dNm4caO0atVKunfvLjk5OV7Tr1u3Tvr37y+DBw+WTZs2SZ8+fcxny5YtHuk04Dl48KDr85///Kec1ggIb+HwAkwAEKc/CVpLfNq3by8zZ840wwUFBVKnTh0ZPny4jBkzpkj6fv36yYkTJ2T58uWucZ06dZLWrVvLnDlzXCVAP/30kyxdurRMy8SToBHNtG3PlU+sLvUFmB8//DueBAwgovhz/Q5pCdCZM2dkw4YNkpaW9tsCVahghjMzM71+R8e7p1daYlQ4/Zo1a6R69erSpEkTGTJkiPzwww/FLsfp06dNprl/gGgVTi/ABIBQCWkAdOTIEcnPz5caNWp4jNfh7Oxsr9/R8aWl1+qvefPmyapVq+SJJ56QtWvXSs+ePc1veTNlyhQTMdofLYECohkvwATgdFH5IMTbb7/d9bc2km7ZsqU0bNjQlApdd911RdKPHTvWtEOyaQkQQRCcEAR1bZbMCzABOFJIA6CkpCSJjY2VQ4cOeYzX4eTkZK/f0fH+pFcNGjQwv7V7926vAVB8fLz5AE7DCzABOFVIq8Di4uKkbdu2pqrKpo2gdTg1NdXrd3S8e3q1cuXKYtOr/fv3mzZANWvyeH8AABAG3eC16un555+XV155RbZv324aLGsvr0GDBpnpAwYMMFVUthEjRkhGRoZMnz5dduzYIRMnTpT169fLsGHDzPTjx4/L6NGj5dNPP5XvvvvOBEu9e/eWRo0amcbSAAAAIW8DpN3aDx8+LBMmTDANmbU7uwY4dkPnvXv3mp5hts6dO8uCBQtk/PjxMm7cOGncuLHp7p6SkmKma5Xal19+aQIq7Qpfq1Yt6datm0yePJlqLgAAEB7PAQpHPAcIAIDIEzHPAQIAAAgFAiAAAOA4BEAAAMBxCIAAAIDjEAABAADHCXk3+HBkd4zjpagAAEQO+7rtSwd3AiAvjh07Zv7nfWAAAETmdVy7w5eE5wB5oa/j+P7776VKlSoSExMTsA1iv2R13759pT6fAORpqLCfkp/hjn2U/CyOlvxo8KMPQXZ/iLI3lAB5oZl28cUXS7Bo8EMARJ6GO/ZT8jPcsY+Sn96UVvJjoxE0AABwHAIgAADgOARA5Sg+Pl7S09N5KSt5GtbYT8nPcMc+Sn4GAo2gAQCA41ACBAAAHIcACAAAOA4BEAAAcBwCIAAA4DgEQGdp1qxZUq9ePUlISJCOHTtKVlaWT99buHChecp0nz59ijzFcsKECVKzZk2pVKmSpKWlyddffy1OEej8vPvuu81490+PHj3ESfzJ07lz5xbJL/2eO6fvo8HIU6fvp/4e9z/99JMMHTrU7IPaI+zSSy+VFStWnNU8o02g83TixIlF9tGmTZtKRLNQZgsXLrTi4uKsl156ydq6dat17733Wueff7516NChEr+3Z88eq3bt2tZVV11l9e7d22Pa1KlTrcTERGvp0qXWF198Yd10001W/fr1rZ9//jnqt1Qw8nPgwIFWjx49rIMHD7o+R48etZzC3zx9+eWXrapVq3rkV3Z2tkcaJ++jwcpTJ++n/ubn6dOnrXbt2lnXX3+99fHHH5vjf82aNdbmzZvLPM9oE4w8TU9Pt5o3b+6xjx4+fNiKZARAZ6FDhw7W0KFDXcP5+flWrVq1rClTphT7nV9//dXq3Lmz9cILL5iTnvsFu6CgwEpOTramTZvmGvfTTz9Z8fHx1n/+8x8r2gU6P5W3cU7ib57qxVqDm+I4fR8NRp46fT/1Nz9nz55tNWjQwDpz5kzA5hltgpGn6enpVqtWraxoQhVYGZ05c0Y2bNhgiv/d3yGmw5mZmcV+79FHH5Xq1avL4MGDi0zbs2ePZGdne8xT32mixZclzTMaBCM/bWvWrDFpmjRpIkOGDJEffvhBnKCseXr8+HGpW7eueXFv7969ZevWra5pTt5Hg5WnTt5Py5Kfb775pqSmpprqmho1akhKSoo8/vjjkp+fX+Z5RpNg5KlNq7r1JaMNGjSQO+64Q/bu3SuRjACojI4cOWJ2Dt1Z3OmwXiC8+fjjj+XFF1+U559/3ut0+3v+zDNaBCM/lbajmDdvnqxatUqeeOIJWbt2rfTs2bPIgR2NypKnevF96aWXZNmyZTJ//nwpKCiQzp07y/79+8Xp+2iw8tTJ+2lZ8vPbb7+V1157zXxP26g88sgjMn36dHnsscfKPM9oEow8VXqTo+3ZMjIyZPbs2eZm6KqrrjJvXo9UvA2+nOhOctddd5mLdVJSUnn9rDg9P2+//XbX3y1atJCWLVtKw4YNzd32ddddV05LGzn0LlA/Nr1QX3bZZfLss8/K5MmTQ7ps0Zyn7Ke+0wBSS8qee+45iY2NlbZt28qBAwdk2rRp5lVDCE6e9uzZ05Vez6MaEGmp5v/+7/+WWAIfzgiAykgvurqjHDp0yGO8DicnJxdJ/80338h3330nvXr18tjpzEaoWFF27tzp+p7OQ1viu8+zdevWEs2CkZ8a6BSmRbf6W7t37476AMjfPPXmnHPOkTZt2pj8Uk7eR4OVp944ZT8tS37qfqd5qN+zaUCppRta/ROIbRTJgpGncXFxRb5z/vnnm55iJe3H4Y4qsDLSHUKjZC2ydr8A67D73Z5Nuwt+9dVXsnnzZtfnpptukmuvvdb8rW0D6tevb3ZQ93nm5eXJZ5995nWe0SQY+emNVjto2wr3i3e08jdPvdEicc1nO7+cvI8GK0+dvJ+WJT+vuOIKc9G1b3jUrl27TF7p/AKxjSJZMPK0uHZteiMa0ftoqFthR3pXQ+39MnfuXGvbtm3WH//4R9PV0O7ietddd1ljxozxq+eHdjHWeSxbtsz68ssvzXSndDEOdH4eO3bMeuihh6zMzEzTrfP999+3Lr/8cqtx48bWqVOnLCfwN08nTZpkvfvuu9Y333xjbdiwwbr99tuthIQE05XW5uR9NBh56vT91N/83Lt3r1WlShVr2LBh1s6dO63ly5db1atXtx577DGf5xntgpGnf/7zn03XeN1HP/nkEystLc1KSkqycnJyrEhFAHSWnn76aeuSSy4xz1zQroeffvqpa1qXLl3MRdmfAEi7GT/yyCNWjRo1zA583XXXmR3SKQKZnydPnrS6detmXXTRRdY555xj1a1b1zwPwyknwbLk6ciRI11pdR/U54Js3LjRY35O30cDnafsp/4f9+vWrbM6duxo9j/tvv23v/3NPBLD123kBIHO0379+lk1a9Y089Pnrunw7t27rUgWo/+EuhQKAACgPNEGCAAAOA4BEAAAcBwCIAAA4DgEQAAAwHEIgAAAgOMQAAEAAMchAAIAAI5DAAQgaPTt0frOICeLiYmRpUuXlutv6nvy9Hf1tTBno169ejJjxoywWz8gEAiAgAhw9913mwuNfvSlhTVq1JCuXbvKSy+95PH+Hl9MnDgxKC8u9Xax7Nevn3mnULBdc801rvzRj+bPbbfdJv/973/L7TcLf3Q6gPBFAAREiB49esjBgwfN3f0777xjXvw6YsQIufHGG+XXX3+VcFSpUiWpXr16ufzWvffea/Ln+++/l2XLlsm+ffvkzjvvDNrvvfHGG+b39JOVlWXGvf/++65xOr0s9OH84bo9gWhCAAREiPj4ePMm9tq1a8vll18u48aNMxd6DYa0qsn2008/yT333CMXXXSRVK1aVX73u9/JF198YaZpukmTJplhu6TC/m5J37O99dZb0r59e0lISJCkpCS5+eabzXgt7dDSlgcffNA13+KqwGbPni0NGzY0b5lu0qSJvPrqqx7T9bsvvPCCmfe5554rjRs3ljfffLPU/NG0mj/6dupOnTrJsGHDZOPGjR5p1q5dKx06dDB5qenGjBnjCjbmzZsnlStXlq+//tqV/v7775emTZvKyZMni/xetWrVzO/pR/NMXXjhha5xOt125MiRYtdnzZo1Zp11O+pbvHXZPv74Y1OyN2XKFKlfv74JJFu1aiWvvfaa63s//vij3HHHHea3dbrO9+WXX/ZYxm+//dYEyvq7+v3MzEyP6a+//ro0b97c/KaW4E2fPr3EPNa8ufrqq832b9asmaxcubLU7QKErVC/jAxA6by9ONfWqlUrq2fPnq5hfUtzr169rM8//9zatWuXeYvzhRdeaP3www/mxZs63Lx5c+vgwYPmo+NK+57SN0THxsZaEyZMMG+Y3rx5s/X444+baZrm4osvth599FHXfNXLL79sJSYmupbtjTfeMC+mnTVrlnmB6vTp0808V69e7UqjpyWd14IFC6yvv/7aeuCBB6zKlSu7lsMbfbnjiBEjXMOaVtfl2muvdY3bv3+/de6551r333+/tX37dmvJkiXmbdbp6emuNLfddpvVvn1765dffjHrq8u6fv36UrePviFbl3vTpk1FppW2Ph988IFJ07JlS+u9994zL5jUafom7qZNm1oZGRnmTfKal/qiSn0jtxo6dKjVunVrs73091euXGm9+eabHsuj39f10Ly+9dZbzQuBdd2UrleFChXMNtPpOv9KlSqZ/22a/p///Kf5Oz8/30pJSTEvv9Vtv3btWqtNmzbmdzQvgUhDAAREeACkb2W+7LLLzN8fffSRVbVqVevUqVMeaRo2bGg9++yz5m+94GvQ5M6X76Wmplp33HFHscvofrG0FQ6AOnfubN17770eaTTo0Dek2/SCOn78eNfw8ePHzbh33nmnxABIg5XzzjvPBDma/tJLLzWBgG3cuHFWkyZNzNvsbRqIaTCiF3d19OhRE6wMGTLEvLld34jti9ICoJLWxw6Ali5d6kqj20HXQ9/Q7W7w4MFW//79zd8a4A0aNKjE5XnhhRdc47Zu3WrGafCn/vCHP1hdu3b1+N7o0aOtZs2aed2m7777rlWxYkXrwIEDrum6DgRAiFRUgQERTq+xdpWTVlkdP37cVMVodY792bNnj3zzzTfFzsOX72mPouuuu+6slnX79u1yxRVXeIzTYR3vrmXLlq6/zzvvPFMll5OTU+K8tTpIl1HXRauQGjVqJN26dZNjx465fjs1NdWVV/Zv63rv37/fDF9wwQXy4osvuqrptIosEHxZn3bt2rn+3r17t6l204bu7ttDq+ns7TFkyBBZuHChadD+l7/8RdatW1fi72qVn7J/t7htodVc+fn5Real6evUqSO1atVyjdP8BCJVxVAvAICzoxcmbSei9GKuFzptV1JYSd3RffmetjMpL9rTzZ0GLaX1dktMTDRBj9L/NZDRdVq0aJFp2+SrDz/8UGJjY01D5hMnTkiVKlWkPNZHAyP37aHefvtt0+bLnbbXUT179jTtrlasWGHa4mhwOnToUHnyySe9/q4d+PnbaxCIVpQAARFs9erV8tVXX0nfvn3NsDaOzs7OlooVK5ogwP2jjZaVNj4ufIfvy/e0NGHVqlXFLou3+RZ22WWXySeffOIxToe1QW2gaRCjfv75Z9dvayPg/6uV+u23NcC5+OKLzbCWojzxxBOmsbeWuGhD6lDQ/NBAZ+/evUW2h5bC2LQB9MCBA2X+/PnmEQTPPfecz79R3La49NJLXXlXOL32rNPA0Pbpp5+WeR2BUKMECIgQp0+fNkGKBhmHDh2SjIwM00tIu8EPGDDApElLSzPVEn369JG///3v5mKm3cK1JEF7IWk1i/b20aotrS7SC78GAL58Lz093ZQyaNXQ7bffbnpPaenDww8/bH5b56ulJzpNL9524ORu9OjR8vvf/17atGljflMDDe0urt3Hz5ZWGWn+KM2fyZMnm95KWg1m9+jSIGH48OEmsNm5c6dZp1GjRkmFChVMVdldd90lDzzwgCld0bzRHm+9evWSW2+99ayXzx+6TR566CHTq05LbK688krJzc01AYpWn2nQM2HCBNNrTHtx6b6xfPlyE6T46s9//rNZP80nfV6TBoczZ86UZ555xmt63V66X+hvT5s2TfLy8uSvf/1rANcaKGehboQEwLdG0Hq46kcbol500UWm19ZLL73kasBry8vLs4YPH27VqlXLNAyuU6eOaby8d+9eVwPbvn37Wueff76Zn93rp7Tvqddff930PIqLizM9qG655RbXtMzMTNOTSXsq2aeWwo2g1TPPPGM1aNDA/IY2VJ43b57HdG+NanUe7r2TvDWCtvNHPxdccIEZ5967TGkPKu3lpcufnJxsPfzww65eUdqguEWLFh4NwbWXWrVq1UwPsrNpBF3S+tiNoH/88UePNNpYe8aMGabhtuaVbvPu3bub3ldq8uTJpvG79tzSZdRG8t9++22xy6Pz13H6e7bXXnvNNHrW+V9yySXWtGnTSmzYrr3FrrzySpN/uu20hxqNoBGpYvSf8g66AAAAQok2QAAAwHEIgAAAgOMQAAEAAMchAAIAAI5DAAQAAByHAAgAADgOARAAAHAcAiAAAOA4BEAAAMBxCIAAAIDjEAABAADHIQACAACO8/8AkKpORl5NhTYAAAAASUVORK5CYII=",
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",
       "text/plain": [
        "
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@@ -2046,7 +2519,47 @@
     },
     {
      "data": {
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",
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",
+      "text/plain": [
+       "
"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "
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+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "
"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "
"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
       "text/plain": [
        "
"
       ]
@@ -2064,12 +2577,165 @@
     "plt.title(\"Effect of Detection Threshold on Character Error Rate\")\n",
     "plt.show()\n",
     "\n",
+    "plt.scatter(df[\"config/textline_orientation\"], df[\"CER\"])\n",
+    "plt.xlabel(\"Detection Box Threshold\")\n",
+    "plt.ylabel(\"CER\")\n",
+    "plt.title(\"Effect of Detection Threshold on Character Error Rate\")\n",
+    "plt.show()\n",
+    "\n",
+    "plt.scatter(df[\"config/line_tolerance\"], df[\"CER\"])\n",
+    "plt.xlabel(\"Line Tolerance\")\n",
+    "plt.ylabel(\"WER\")\n",
+    "plt.title(\"Effect of Line Tolerance  on Character Error Rate\")\n",
+    "plt.show()\n",
+    "\n",
+    "plt.scatter(df[\"config/text_det_unclip_ratio\"], df[\"CER\"])\n",
+    "plt.xlabel(\"Detection Box Threshold\")\n",
+    "plt.ylabel(\"CER\")\n",
+    "plt.title(\"Effect of Text detection expansion coefficient on Character Error Rate\")\n",
+    "plt.show()\n",
+    "\n",
+    "plt.scatter(df[\"config/text_rec_score_thresh\"], df[\"WER\"])\n",
+    "plt.xlabel(\"Line Tolerance\")\n",
+    "plt.ylabel(\"WER\")\n",
+    "plt.title(\"Effect of Text recognition threshold on Character Error Rate\")\n",
+    "plt.show()\n",
+    "\n",
+    "plt.scatter(df[\"config/text_rec_score_thresh\"], df[\"WER\"])\n",
+    "plt.xlabel(\"Line Tolerance\")\n",
+    "plt.ylabel(\"WER\")\n",
+    "plt.title(\"Effect of Text recognition threshold on Character Error Rate\")\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "id": "cc1e3d53",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "
"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "
"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "
"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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qvasHgwrY6ljoBqvAz7+SRbjzyb+mmyiYVYBW2H4OtR6hRHLtR7ou7nl59tlnh5yPqvJHi9Zb11ZRCvIX59or7vkb7+PqBlRqJkHns160FNSPHTvWjlNgpQLaCxYsMPXq1bNBhtoCdNcx1L3Lvc7VPE9wwezg2unuPIKvN13P/i9YhTnqqKMCaoGGo/t6uILm4dbNy/YVRk1QKDDSS+b06dPNyy+/fMg9U9544w1z77332gBv//79RXrZjcazJKUDqmhTCoUi7nC1ChK96mlRt1XUbISi81CCb2K6cEQ3p3Xr1sWklqS7nqq5Ei4gKuhB6z+PBx98MGxzChUrVrSpdvGm1IlQ/puiH71zV1WS3RvSt99+a3JyciJav+Ablx7UevioYcmHHnrI1K1b196o9daqh5O774uyfaeffrqd78yZM231/6eeesrOS1Wp3YdYpOsX7f1cFIWti7tv9CYeqmp4vGsxFufaK6n9GovjqoBKPv74Y1+NN/e6UOqegh2NW7t2bcD0xRHuBSZWClp+NNatfBHnceKJJ/oCQTV1oBez/v37232se4p89NFH5k9/+pO9P0yaNMnUqlXLpvY/++yzthZmLJ4l/gioCqG3b0XIhUX4mk7BhR7ABb2pFiWLSG89SmHQQfePzNVwnzs+mo455hj7VydkJG80aqVZDzY9sP/v//7PnpCqNux/0y/q9mpb3Tdn1/Llyw95Q6tUqZLNtipsPcMt300F0xt/QfPQsjSN2j4pznLieVwjPXdFb9fKWlCVbwU/blAdal2UiuL/tqmUTW2L2/7WrFmzbGD2+uuvB6QOeM2u0nWlLAl9tF26iaox1XABlXs+aX39q72rRwS9lUZzPxd27Ue6Lu55qbaMIjluXtdb16va+dE1H4miXHtFUZz7REkfV/c4uEGTUluUfej/0qhUKqVe6WVSgZsbbLnrEHzvcq9zBWOFNRvgzkPb6d6b3VSV4qbuRUs0tq+o7r//fptSdd9999kXKVF2nZr10PWnLEuXAqpIzrFon88pV4Yq2lSeSNkWbkqMP128SvJ185j1xqPGHQt6E9JJ5nYPUZjzzjvP5t1PmzbNN0zL++c//2lTT1QmJZp081DDlI8//rh9wAbThezSNuhBpixWlbVRYKW85uByN9re4DIzBbUhJsFZh8EtKOvGpf2tiylUoOO/nu5FHbzPW7dubR8o48aNsw/ncPNQwKO3IwUJX375ZdhjG2458TyukZ67ojc/PaSU7aekbwXFV199dci3eJVT9Kf19j9+boqA/291DoS6yUVK5dz8aT8ptdQ/iT/Ufg51/ijVTM4//3wTDZFc+5Gui4JYBfC6jhToFHRuR2O9VVZEZSHDrXewolx7RVGU+0SsjqtLKSLKTlLKqFt+yqXvusaUUqKUDD2cRSklSvl+7rnnAu4J2meaj7sNBdEDXoGuri//45EILcpHY/uKSvdrnXvqEcQt06bzUYGSAiKXih6EahE91LM32udzyqVQqQC6+6YffOL7R/mRuu222+yb9gUXXGAL4OlBrLxhZYm88sor9uApGlcBP5XpUDCgNwqV8dADSheaxt1www12fvq9Chvq4le5C73pqyBjKCoorOBGy124cKFNAdAy9Uaki8q9eKNJD0vdQJTcqoes9pne/HTT0FvY119/bae76aab7ENO26KTUturAEt52Srr0qJFC9/2KnBQAVIVJtSDUAUAQ9EFqsK4SrrVzVXHbO7cuTYFJNTbilI8tO+0nnpzVAqBgjqtk5tVp4tQb5R6o9H+0kWl32i/KwhUEHDCCSfYVA+VIVChe81XDzUFUaKHm24SCnR0TPRWrIBThb715qr5a921Hx544AG77npbUjkYBanxOq6RnrsKdFSQVzcqlbcQ3cSVDD558mQzcODAgPmuXr3aJrPrmOu8mDp1qi3c7x5zN5VLx/mvf/2rDVjVBYr2RahAPRI6vgr2tQ1KBVJwq21wr6tQtD5KNVWAqBupjp/Ku+ghoCA5uFBucUVy7Ue6LjrvtM81v1atWpmePXvat+g1a9bYY6SC0KECoOJQ69lqJFHXptZF5VV0fuj60TEPV8Ek0muvKIpyn4jVcXXpfqhrRC3TB1fI0D1K17s+wYX7VZxA9xelWunlRC3O67pS+cFI+kbUcVdK8ZgxY+w1rCBFheD1jNN1Gyl1g6NrNJjKvapIQHF53b7i3tP+9a9/2fukzkMFz3qW6prTPUjlnfQM08uWcgH8hXv2RvV8dtKg2YTgavNFaTbBrcqsRuNUbVPVWNXWhpoRUFsz/m0BqV0XNR9w3HHH2enUpofapVm4cKFvmu+//96266SG0yKp+qrGFtXgmJapeapJg+AmAArapuI07Kl2lVTtVe2SqK0kNSp3wQUX+Bqxc6vt+zdgJ6rur/VQFXd3v6g9LDV7oKrg+k1hVaPV0KCqn6upAFVzLahhT+0bHS+1H6L11Pqq2YcnnngiYDqtr9pAUZspweeCGqhTW1danqpSa/3UMKnaqPKndle0T3RMNZ2q/2vZ/tXB1T6ShqvJgUga9izsuPo3qhcs1P4IpbBzV/tWVYO1n4OpPSMdA7VT5V/9WQ33qYq4GvZUmzM33HDDIQ17vv7667YNLDW0pzaU1JaSW03fv42acOdt8D5Tw5BqX0bnka4dXWP33XdfwPUXrmFPtY+jpjB0juhcKagByMLWI5xIrv1I10V07qiZCR0b7UNV3VdbV2p4MVrNJoiqhN91112+ddI1pGPrttUmxb32wrUn5J7X/ud7Ue8TsTqusnz5ct9zJLgJEjVn4K6zGjANpnYR1T6fzlk1PaDrLFzDl8HHy23fT9upav3Fbdgz3Md/+92GPYMVdA+KxvaFEu68cWkfaFm7du2y39UAp9swra4/nVeh7gUFPXsjfZYUJkP/FCtUBJBW9NapbC0lgxflDRkA0gFlqAAAADwioAIAAPCIgAoAAMAjylABAAB4RAoVAACARwRUAAAAHqVcw57RoEb5NmzYYBtYLEqXCAAAIH7UEtTu3btt452hOlMuSQRUISiYcjtfBAAAyWXt2rW+nh9ihYAqBLfrDx0QdQUBAAASX15enk0QKYmu2QpDQBWCm82nYIqACgCA5JIRh+I6FEoHAADwiIAKAADAIwIqAAAAjwioAAAAPCKgAgAA8IiACgAAwCMCKgAAAI8IqAAAADwioAIAAPCIltIBAEBMHDjomAWrd5gtu/NNjUqZpl2DqqZ0qdi3al4SCKgAAECJm7Nkoxk1a6nZmJvvG1YrK9OM6NbUdGlWK+mPAFl+AACgxIOpAVMXBQRTsik33w7X+GRHQAUAAEo0m2/UrKXGCTHOHabxmi6ZEVABAIASs2D1jkNSpvwpjNJ4TZfMCKgAAECJ2bI7P6rTJSoCKgAAUGJqVMqM6nSJioAKAACUmHYNqtrafOEaR9Bwjdd0yYyACgAAlJjSpTJs0wgSHFS53zU+2dujIqACAAAlqkuzWmZy71YmOyswW0/fNTwV2qGiYU8AAFDiujSrZTo1zaaldAAAAC9Kl8owOQ2rpeROJMsPAADAIwIqAAAAjwioAAAAPCKgAgAA8IiACgAAwCMCKgAAAI8IqAAAADwioAIAAPCIgAoAAMAjAioAAACP6MsPAAB4cuCgk7J99EWKgAoAABTbnCUbzahZS83G3HzfsFpZmWZEt6a2Q+R0QZYfAAAodjA1YOqigGBKNuXm2+Eany4IqAAASMIstvmrtpuZi9fbv/oej3VQylSoJTt//NX4eKxbPJDlBwBAEkmULDaVmQpOmfKnMErjNV1Ow2om1ZFCBQBAkkikLDYVQI/mdMmOgAoAgCSQaFlsqs0XzemSHQEVAABJoChZbLGgphGU1RiucYSMP7IiNV06IKACACAJJFoWm9qZUrktyQga537X+HRpj4qACgCAJJCIWWwqBD+5dyuTnRW4TH3X8HRqh4pafgAAJAE3i00F0EOVksr4I5CJdRabgqZOTbPTvqX0hEihmjhxoqlfv77JzMw07du3NwsWLAg77WuvvWbatGljqlSpYipUqGBatmxpXnjhhYBprrrqKpORkRHw6dKlSwy2BACA9MtiK10qwzaNcGHLOvZvumTzJVRANW3aNDN48GAzYsQIs2jRItOiRQvTuXNns2XLlpDTV61a1dx1111m/vz55ptvvjH9+vWzn7feeitgOgVQGzdu9H1eeumlGG0RAAAlgyy2xJXhOE5cmzBVilTbtm3NhAkT7PeDBw+aunXrmkGDBpmhQ4dGNI9WrVqZ888/34wePdqXQrVr1y4zY8aMYq1TXl6eycrKMrm5uaZy5crFmgcAACWFzogT7/kd1xSqX3/91SxcuNB07NjxfytUqpT9rhSowigWnDt3rlm+fLk5/fTTA8bNmzfP1KhRwxx77LFmwIABZvv27WHns3//fnsQ/D8AACQqstgST1wLpW/bts0cOHDA1KxZM2C4vn///fdhf6fIs06dOjYQKl26tJk0aZLp1KlTQHbfxRdfbBo0aGBWrVpl7rzzTtO1a1cbpGn6YGPGjDGjRo2K8tYBAIB0kZS1/CpVqmQWL15s9uzZY1OoVAbrmGOOMWeeeaYd37NnT9+0J554omnevLlp2LChTbU655xzDpnfsGHD7DxcSqFStiMAAEDCB1TVq1e3KUabN28OGK7v2dnZYX+nbMFGjRrZ/6uW37Jly2wqkxtQBVOwpWWtXLkyZEBVrlw5+wEAAEi6MlRly5Y1rVu3tqlMLhVK1/ecnJyI56PfKPsvnHXr1tkyVLVqpU8DYwAAII2y/JTV1rdvX9u2VLt27cz48ePN3r17bVMI0qdPH1teSilQor+aVll4CqJmz55t26GaPHmyHa9sQJWHuuSSS2wql8pQ3X777TZFS80xAAAApFxA1aNHD7N161YzfPhws2nTJpuFN2fOHF9B9TVr1tgsPpeCrYEDB9pUp/Lly5vjjjvOTJ061c5HlIWo9qmee+4523RC7dq1zbnnnmubVCBbDwAApGQ7VImIdqgAAEg+eXFshyruKVQAACA+aCA0egioAABIQ3OWbDSjZi01G3PzfcPU+bL6A1QXN0iyvvwAAEDsg6kBUxcFBFOyKTffDtd4FA0BFQAAaZbNp5SpUAWo3WEar+kQOQIqAADSyILVOw5JmfKnMErjNR0iR0AFAEAa2bI7P6rT4b8IqAAASCM1KmVGdTr8FwEVAABppF2DqrY2X0aY8Rqu8ZoOkSOgAgAgjZQulWGbRpDgoMr9rvGaDpEjoAIAIM2onanJvVuZ7KzAbD1913DaoSo6GvYEACANKWjq1DTb1uZTAXSVmVI2HylTxUNABQBAmlLwlNOwWrxXIyWQ5QcAAOARARUAAIBHBFQAAAAeEVABAAB4RKF0AACSjDoupnZeYiGgAgAgicxZstGMmrU0oINjtWyuxjhpPyp+yPIDACCJgqkBUxcFBFOyKTffDtd4xAcBFQAASZLNp5QpJ8Q4d5jGazrEHgEVAABJQGWmglOm/CmM0nhNh9gjoAIAIAmoe5hoTofoIqACACAJqK+9aE6H6CKgAgAgCajjYtXmywgzXsM1XtMh9gioAABIko6M1TSCBAdV7neN13SIPQIqAACShNqZmty7lcnOCszW03cNpx2q+KFhTwAAkoiCpk5Ns2kpPcEQUAEAkGSUrZfTsFq8VwN+yPIDAADwiIAKAADAI7L8ACDBqSsRtX6tBhvVxpCqxVOTC0gsBFQAkMDU2a36Z/PvckRtDal6PDW6gMRBlh8AJHAwNWDqokP6b9uUm2+HazyAxEBABQAJms2nlCl1eBvMHabxmg5A/BFQAUACUpmp4JQpfwqjNF7TAYg/AioASEAqgB7N6QCULAIqAEhAqs0XzekAlCwCKgBIQGoaQbX5wnVzq+Ear+kAxB8BFQAkILUzpaYRJDiocr9rPO1RAYmBgAoAEpTamZrcu5XJzgrM1tN3DacdKiBx0LAnACQwBU2dmmbTUjqQ4AioACDBKVsvp2G1eK8GgAKQ5QcAAOARARUAAIBHBFQAAAAeEVABAAB4REAFAADgEQEVAACARwRUAAAAHhFQAQAAeETDngAAxMCBgw4t3qcwAioAAErYnCUbzahZS83G3HzfsFpZmbaDa/pkTA1k+QEAUMLB1ICpiwKCKdmUm2+HazySHwEVAAAlmM2nlCknxDh3mMZrOiQ3AioAAErIgtU7DkmZ8qcwSuM1HZIbARUAACVky+78qE6HxEVABQBACalRKTOq0yFxUcsPAIASat5Av1VtPhVAD1VKSnPNzvrvMpDcCKgAACih5g0UiOm3qs2n4Mk/qHJDNI0vbsCGxEGWHwAAJdi8gQKxyb1b2ZQof/qu4bRDlRpIoQIApL3CmjdQ+pHGd2qaXazUJAVN+m20shKReAioAABpryjNG+Q0rFas/aXgqbi/ReIjyw8AkPZo3gBeEVABANJepM0W/LRtX9rvKyRwQDVx4kRTv359k5mZadq3b28WLFgQdtrXXnvNtGnTxlSpUsVUqFDBtGzZ0rzwwgsB0ziOY4YPH25q1aplypcvbzp27GhWrFgRgy0BACQjt3mDwko0jX/3B/reQ2IGVNOmTTODBw82I0aMMIsWLTItWrQwnTt3Nlu2bAk5fdWqVc1dd91l5s+fb7755hvTr18/+3nrrbd804wdO9Y8+uij5rHHHjOff/65Dbw0z/x8WqIFAIRv3iCSHvXoew+hZDhKzokjpUi1bdvWTJgwwX4/ePCgqVu3rhk0aJAZOnRoRPNo1aqVOf/8883o0aNt6lTt2rXNrbfeaoYMGWLH5+bmmpo1a5opU6aYnj17Fjq/vLw8k5WVZX9XuXJlj1sIAEgWj7z7g3n43cJzNF7qfzIFzBNQXhyf33FNofr111/NwoULbZacb4VKlbLflQJVGAVPc+fONcuXLzenn366HbZ69WqzadOmgHlq5ypwCzfP/fv324Pg/wEApJ/61StENB197yGhAqpt27aZAwcO2NQjf/quoCgcRZ4VK1Y0ZcuWtSlT//znP02nTp3sOPd3RZnnmDFjbNDlfpRCBgBIP/S9h6QtQ1UclSpVMosXLzZffPGFue+++2wZrHnz5hV7fsOGDbNBmvtZu3ZtVNcXAJAahdM1XOPpew8J1bBn9erVTenSpc3mzZsDhut7dnZ22N8pW7BRo0b2/6rlt2zZMpvKdOaZZ/p+p3molp//PDVtKOXKlbMfAEB6o+89JGUKlbLsWrdubctBuVQoXd9zcnIino9+o3JQ0qBBAxtU+c9TZaJU268o8wQApCf63kNSdj2j7Lq+ffvatqXatWtnxo8fb/bu3WubQpA+ffqYOnXq2BQo0V9N27BhQxtEzZ4927ZDNXnyZDs+IyPD3Hzzzebee+81jRs3tgHWPffcY2v+de/ePa7bCgBIDvS9h6QLqHr06GG2bt1qG+JUoXFly82ZM8dXqHzNmjU2i8+lYGvgwIFm3bp1ttHO4447zkydOtXOx3X77bfb6a699lqza9cu06FDBztPNRwKAEAk6HsPSdUOVSKiHSoAAJJPXhzboYp7ChUA4H8OHHTMgtU7bDtHqsKv2mRKKQGQ2AioACBBzFmy0XZrsjH3f91kqYq+ukRRmR4AiSsp26ECgFQMpgZMXRQQTMmm3Hw7XOMBJC4CKgBIgGw+pUyFKtDqDqNDXiCxEVDF+KY5f9V2M3PxevtX3wFAZaaCU6b86U6h8ZoOQGKiDFWMUDYCgNeOdumQF0hcpFDFAGUjABSEDnmB5EdAVcIoGwGgMHTICyQ/AqoSRtkIAJF2yCvBLU653zWe9qiAxEVAVcIoGwEgEnTICyQ3CqWXMMpGAIgUHfICyYuAKkZlI9Q4X6hGEpScn5313+4lAIAOeYHkRJZfCaNsBAAAqY+AKgYoGwEAQGojyy9GKBsBAEDqIqCKIcpGAACQmsjyAwAA8IiACgAAwCOy/IAYdUGkVvPV0KvaJlMzGbR6DQCpg4AKiEHn2KNmLTUbc/N9w9Q2mboSUWUFAEDyI8sPKOFgasDURQHBlKihVw3XeACJl6I8f9V2M3PxevtX34HCkEIFlBDdhJUyFepW7PzRSr7Gd2qaTfYfkCBIUUZxkUIFlBCVmQpOmQoOqjRe0wGIP1KU4QUBFVBCVAA9mtMBiF+Ksmg82X8Ih4AKKCGqzRfN6QCUHFKU4RUBFVBC1DSCavOprFQoGq7xmg5AfJGiDK8IqIASonam1DSCBAdV7neNpz0qIP5IUYZXBFRACVI7U5N7tzLZWYHZevqu4bRDBSQGUpThFc0mACVMQZOaRqCldCDxU5TVPpxSkP0Lp5OijEhkOI5Di2VB8vLyTFZWlsnNzTWVK1eOaEcCAJIf7VAlt7w4Pr9JoQIA4A+kKKO4CKgAAAjK/stpWI19giKhUDoAAIBHBFQAAAAeEVABAAB4REAFAADgEQEVAACARwRUAAAAHhFQAQAAeERABQAA4BEBFQAAgEcEVAAAAIkUUOXn55tx48ZFc5YAAAAJr8gB1datW80bb7xh3n77bXPgwAE77LfffjOPPPKIqV+/vrn//vtLYj0BAABSo3Pkjz/+2FxwwQUmLy/PZGRkmDZt2phnn33WdO/e3ZQpU8aMHDnS9O3bt+TWFgAAINlTqO6++25z3nnnmW+++cYMHjzYfPHFF+aiiy4yf//7383SpUvNddddZ8qXL19yawsAAJCAMhzHcSKduFq1auajjz4yTZs2Nb/88oupWLGiee2118yFF15oUolS4LKyskxubq6pXLlyvFcHABLCgYOOWbB6h9myO9/UqJRp2jWoakqXyoj3agEJ8fwuUpbfzp07TfXq1e3/lRJ1+OGHm2bNmpXUugEAEsScJRvNqFlLzcbcfN+wWlmZZkS3pqZLs1pxXTcgERQpoBJl7W3atMn+X4lby5cvN3v37g2Ypnnz5tFbQwBA3IOpAVMXmeDsjE25+Xb45N6tCKqQ9oqU5VeqVClbGD3UT9zh+uvW/ktWZPkBwP+y+To88F5AypQ/ZfhlZ2Waj+84m+w/xF3SZPmtXr265NYEAJBwVGYqXDAler3WeE2X07BaTNcNSCRFCqjq1atXcmsCAEg4KoAezemAVFWkZhPGjh1ra/e5PvnkE7N//37f9927d5uBAwdGdw0BAHGj2nzRnA5IVUUKqIYNG2aDJlfXrl3N+vXrfd/37dtnHn/88eiuIQAgbtQ0gmrzhWscQcM1XtMB6axIAVVwYfQilGcHACQhtTOlphEkOKhyv2s87VEh3UW1c2QAQOpRO1NqGkG1+fzpO00mAMVshwoAkJ5BVaem2bSUDkQroHrqqadslzPy+++/mylTpvhaT/cvXwUASC3K1qNpBCAKDXvWr1/fNtyZ6u1V0bAnAADJJy9ZGvb86aefSm5NAAAA0qFQ+nvvvWeaNm1qI8BgigZPOOEE89FHH0Vz/QAAAFIroBo/frzp379/yGQ0JbH99a9/NQ899FA01w8AkGB9+81ftd3MXLze/tV3AEXM8vv666/NAw88EHb8ueeea8aNG8d+BYAUNGfJRjNq1tKAvv3UqKfaoVItQCCdFSmFavPmzeawww4LO75MmTJm69at0VgvAECCBVMDpi46pKPkTbn5drjGA+msSAFVnTp1zJIlS8KO/+abb0ytWrylAEAqZblpGUqZCrUkd5jGk/2HdFakgOq8884z99xzj8nPP7RXcXWaPGLECHPBBRcUeSUmTpxom2TIzMw07du3NwsWLAg77ZNPPmlOO+00c8QRR9hPx44dD5n+qquuss07+H+6dOlS5PUCgESiVKAOD7xnej35mbnp5cX2r76XdOrQgtU7DkmZCg6qNF7TAemqSAHV3XffbXbs2GGaNGlixo4da2bOnGk/Kld17LHH2nF33XVXkVZg2rRpZvDgwTYYW7RokWnRooXp3Lmz2bJlS8jp582bZ3r16mXef/99M3/+fFO3bl1bdsu/k2ZRALVx40bf56WXXirSegFAIolnltuW3flRnQ4w6d6wp/z8889mwIAB5q233vJ1jqwUIAVBSmlq0KBBkVZAKVJt27Y1EyZMsN8PHjxog6RBgwaZoUOHFvr7AwcO2JQq/b5Pnz6+FKpdu3aZGTNmmOKgYU8AiURZaUqJCpdKlPFHv3of33F2iXRSrKxFpYYV5qX+J9OSOuIqaRr2lHr16pnZs2ebnTt3mpUrV9qgqnHjxjaoKapff/3VLFy40AwbNsw3rFSpUjYbT6lPkdi3b5/57bffTNWqVQ9JyapRo4Zdr7PPPtvce++9plq1aiHnsX//fvtxhWpnCwDipShZbiXRNUy7BlVtbT6lhjkFBHSaDkhXRcry86dARSlL7dq1K1YwJdu2bbMpTDVr1gwYru+bNm2KaB533HGHqV27tg3C/LP7nn/+eTN37lybHfnBBx+Yrl272mWFMmbMGBvRuh+lkAFAooh3lptSvdQ0ggSnf7nfNb4kUseAlA+oEsH9999vXn75ZTN9+nRboN3Vs2dP86c//cmceOKJpnv37uaNN94wX3zxhU21CkUpZEoedD9r166N4VYAQMFqVMqM6nTFoXamJvduZVOi/Om7htMOFdJdkbP8oql69eqmdOnStn0rf/qenZ1d4G/VgKgCqnfffdc0b968wGmPOeYYuyxlUZ5zzjmHjC9Xrpz9AEAiSpQsNwVNnZpm26xFpYYpgNMySZkC4pxCVbZsWdO6dWubNedSoXR9z8nJCfs71TAcPXq0mTNnjmnTpk2hy1m3bp3Zvn07bWQBSEqJlOWmZaic1oUt69i/BFNAgmT5qckEtS313HPPmWXLltkahHv37jX9+vWz41Vzz7/QuspEqS2sZ555xrZdpbJW+uzZs8eO19/bbrvNfPbZZ+ann36ywdmFF15oGjVqZGsiAkAyIssNSGxxzfKTHj162O5qhg8fbgOjli1b2pQnt6D6mjVrbM0/1+TJk23twEsvvTRgPmrHauTIkTYLUS22K0BT0wkqsK52qpSiRbYegGRGlhuQQu1QpQPaoQIAIPnkxbEdqrhn+QEAACQ7AioAAACPCKgAAAA8IqACAADwiIAKAADAIwIqAACAZG+HCgDS0YGDDl24ACmEgAoAYmzOko1m1KylZmNuvm+Y+upT9zF0MgwkJ7L8ACDGwdSAqYsCgilRx8carvEAkg8BFQDEMJtPKVOhuqdwh2m8pgOQXAiogCShh+z8VdvNzMXr7V8euslnweodh6RM+VMYpfGaDkByoQwVkAQoc5MatuzOj+p0ABIHKVRAgqPMTeqoUSkzqtMBSBwEVEACo8xNamnXoKqtzZcRZryGa7ymA5BcCKiABEaZm9RSulSGbRpBgoMq97vGazoAyYWACkhglLlJPWpnanLvViY7KzBbT981nHaogOREoXQggVHmJjUpaOrUNJuW0oEUQkAFJEGZGzX6GKploow/UjYoc5N8lK2X07BavFcDQJSQ5QckMMrcAEByIKACEhxlbgAg8ZHlByQBytwAQGIjoAKSBGVuACBxkeUHAADgEQEVAACARwRUAAAAHhFQAQAAeERABQAA4BEBFQAAgEcEVAAAAB4RUAEAAHhEQAUAAOARARUAAIBHBFQAAAAeEVABAAB4REAFAADgEQEVAACAR2W8zgAAEB8HDjpmweodZsvufFOjUqZp16CqKV0qg8MBxAEBFQAkoTlLNppRs5aajbn5vmG1sjLNiG5NTZdmteK6bkA6IssPAJIwmBowdVFAMCWbcvPtcI0HEFsEVACQZNl8SplyQoxzh2m8pgMQOwRUAJBEVGYqOGXKn8Iojdd0AGKHMlQxRiFSAF6oAHo0pwMQHQRUMUQhUgBeVa9QLqLpVOsPQOyQ5RcjFCIFEI37yK3//rrAaTL+qO2nJhQAxA4BVQxQiBRAtF7KNuWFz8pzW6BS0wm0RwXEFgFVDFCIFEBJvZT5q3J4GfOXU+ubrPJlqeUHxBhlqGKAQqQASvKlzLVz3+/m6U9+sh8a+QRiixSqGIi0cCiFSAFEq8YejXwCsUVAFQMqHKq3xXA9bFGIFEC0X7Zo5BOILQKqGFDhUBUSleCgikKkALy+lIVDI59A7BBQxYg6K53cu5XJzgp809R3DaczUwDFeSmLBI18AiWPQukxpKCpU9NsW8BUNzgl4+vNk+rNACK5f+jlS7X9Iimg7o/ymUDJI6CKMQVPOQ2rxXqxAFLwpax6xXLm1n8tNpvz9odsUiHjj1RwGvkESh4BFQAk8UvZyD+dYBv8VPDkH1RRPhOILcpQAUASo3wmkBhIoQKAIrZanmjlICmfCcQfARUAFKE/veBC4YnSIjnlM4H4IssPAIrQOXFwDTtaJAdAQAUAHjsnpkVyAARUABCFzolpkRwAWX4AEKWWxmmRHEhfBFQAEKWWxmmRHEhfBFQA4LFzYg3XeFokB9IXARUAeOicmBbJASRMQDVx4kRTv359k5mZadq3b28WLFgQdtonn3zSnHbaaeaII46wn44dOx4yveM4Zvjw4aZWrVqmfPnydpoVK1bEYEsApCpaJAeQ0A17Tps2zQwePNg89thjNpgaP3686dy5s1m+fLmpUaPGIdPPmzfP9OrVy5xyyik2AHvggQfMueeea7777jtTp04dO83YsWPNo48+ap577jnToEEDc88999h5Ll261P4GAIqDFskBhJPhKDknjhREtW3b1kyYMMF+P3jwoKlbt64ZNGiQGTp0aKG/P3DggE2p0u/79OljU6dq165tbr31VjNkyBA7TW5urqlZs6aZMmWK6dmzZ6HzzMvLM1lZWfZ3lStXjsJWAgCAkhbP53dcs/x+/fVXs3DhQpsl51uhUqXs9/nz50c0j3379pnffvvNVK1a1X5fvXq12bRpU8A8tXMVuEU6TwAAgKTJ8tu2bZtNYVLqkT99//777yOaxx133GFTpNwASsGUO4/gebrjgu3fv99+/CNcAACApCqUXlz333+/efnll8306dM9lY0aM2aMTcVyP8pyBAAASIqAqnr16qZ06dJm8+bNAcP1PTs7u8Dfjhs3zgZUb7/9tmnevLlvuPu7osxz2LBhNr/V/axdu9bDVgEAgHQT14CqbNmypnXr1mbu3Lm+YSqUru85OTlhf6dafKNHjzZz5swxbdq0CRinWn0KnPznqSy8zz//POw8y5UrZwuv+X8AAACSptkENZnQt29fGxi1a9fONpuwd+9e069fPzteNffUHIKy5UTNJKiNqRdffNG2XeWWi6pYsaL9ZGRkmJtvvtnce++9pnHjxr5mE1TOqnv37nHdVgAAkJriHlD16NHDbN261QZJCo5atmxpU57cQuVr1qyxNf9ckydPtrUDL7300oD5jBgxwowcOdL+//bbb7dB2bXXXmt27dplOnToYOdJG1QAACAl26FKRLRDBQBA8slL13aoAAAAUgEBFQAAAAEVAABAfJFCBQAA4BEBFQAAgEcEVAAAAB4RUAEAAHhEQAUAAOARARUAAIBHBFQAAAAeEVABAAB4REAFAADgEQEVAACARwRUAAAAHhFQAQAAeERABQAA4BEBFQAAgEcEVAAAAB4RUAEAAHhEQAUAAOARARUAAIBHBFQAAAAeEVABAAB4REAFAADgEQEVAACARwRUAAAAHhFQAQAAeERABQAA4BEBFQAAgEcEVAAAAB4RUAEAAHhEQAUAAOARARUAAIBHBFQAAAAeEVABAAB4REAFAADgEQEVAACARwRUAAAAHhFQAQAAeERABQAA4BEBFQAAgEcEVAAAAB4RUAEAAHhUxusMABTuwEHHLFi9w2zZnW9qVMo07RpUNaVLZbDrACBFEFABJWzOko1m1KylZmNuvm9YraxMM6JbU9OlWS32PwCkALL8ENdUm/mrtpuZi9fbv/qeisHUgKmLAoIp2ZSbb4drPAAg+ZFChbhIh1QbBYjaxlBhooYpw0/jOzXNJvsPAJIcKVSIuXRJtVGZqeBtDA6qNF7TAQCSGwEVEirVRjQ+FbL/VAA9mtMBABIXARViKp1SbVSbL5rTAQASFwEVYiqdUm3UNILKhYVrHEHDNV7TAQCSGwEVYiqdUm3UzpQK2UtwUOV+13jaowKA5EdAhZhKt1Qb1Vic3LuVyc4KDBD1XcNTpUYjAKQ7mk1AXFJtVJtPwZOTBqk2CprUNAItpQNA6spwHCf5q1NFWV5ensnKyjK5ubmmcuXK8V6dlJQO7VABANLn+U0KFeKCVBsAQCohoEJcOwjOaViNIwAASHoEVIgJsvgAAKmMWn5pLFadE6dLVzMAgPRFClWailWKER0EAwDSASlUaSiWKUbx7momVqlwAID0RgpVmol1ilE8u5qh3BYAIFZIoUozsU4xildXMwWlwl03dZH526zvSLECAEQNKVRJ3ORAcVKQYp1i5HY1o0AmVKpYxh/dsESzq5nCUuHkmU9+sh8aEwUARAMBVRKIZtZVrFOM4tHVTGGpcKHKjdGvHgAgqbP8Jk6caOrXr28yMzNN+/btzYIFC8JO+91335lLLrnETp+RkWHGjx9/yDQjR4604/w/xx13nElW0S5AHo/OiYvbQXBxC5QXJXXNnaMCVgqsAwCSMoVq2rRpZvDgweaxxx6zwZQCpM6dO5vly5ebGjVqHDL9vn37zDHHHGMuu+wyc8stt4Sd7wknnGDeffdd3/cyZZIzIa4kCpB7TTEqbtZjUbua8ZIqV9TUNf9yY7TcDgAojrhGGg899JDp37+/6devn/2uwOo///mPeeaZZ8zQoUMPmb5t27b2I6HG+wdQ2dnZJp0KkBclEHBTjIIDluxCAhavWY8KniJZTzdVzilm9lxh5bZiWdMQAJAe4hZQ/frrr2bhwoVm2LBhvmGlSpUyHTt2NPPnz/c07xUrVpjatWvbbMScnBwzZswYc/TRR4edfv/+/fbj31t1IijJAuTFSTHyEuTEMlWuoFS4WNY0BACkj7iVodq2bZs5cOCAqVmzZsBwfd+0aVOx56uswylTppg5c+aYyZMnm9WrV5vTTjvN7N69O+xvFHBlZWX5PnXr1jWJoKQLkLspRhe2rGP/FpTNV1ituWiVQYpWsw7hym3FqtwYACC9xL1QerR17drVlrFq3ry5LY81e/Zss2vXLvOvf/0r7G+USpabm+v7rF271iSCeBQgj3fbVdFMlVNQ9fEdZ5uX+p9srj61fshpSqqmIQAgvcQtoKpevbopXbq02bx5c8BwfY9m+acqVaqYJk2amJUrV4adply5cqZy5coBn0TgZl1JRhwDgVi2XRXtVDk3Fe6ebieYx3q3sgFoUWoaAgCQ0GWoypYta1q3bm3mzp1runfvbocdPHjQfr/hhhuitpw9e/aYVatWmSuvvNIko+IWII+mWLZdVZINgRa13BgAAElRy09NJvTt29e0adPGtGvXzjabsHfvXl+tvz59+pg6derYMk5uQfalS5f6/r9+/XqzePFiU7FiRdOoUSM7fMiQIaZbt26mXr16ZsOGDWbEiBE2JaxXr14mWcU7EIhla+cl3RBopDUNAQBImoCqR48eZuvWrWb48OG2IHrLli1tYXK3oPqaNWtszT+XAqSTTjrJ933cuHH2c8YZZ5h58+bZYevWrbPB0/bt282RRx5pOnToYD777DP7/2QWz0Ag1q2dJ0KqHAAARZHhOI73qlkpRs0mqLafCqgnSnmqVOsCJ5b9FwIA0kNeHJ/fBFQJdkASHUEOACBR5cXx+Z2cfbIgbiiDBABAGrRDBQAAEGsEVAAAAB4RUAEAAHhEQAUAAOARARUAAIBHBFQAAAAeEVABAAB4REAFAADgEQEVAACAR7SUHoLbvaGasAcAAMkh74/ndjy6KSagCmH37t32b926dWN9PAAAQBSe4+rTL5boHDmEgwcPmg0bNphKlSqZjIyMqEXNCtDWrl2bVh0us90c73TAeZ5e57lwzBPzmDuOY4Op2rVrm1KlYluqiRSqEHQQjjrqqBLZ4ToBE/EkLGlsd3rheKeXdD3e6bztibzdWTFOmXJRKB0AAMAjAioAAACPCKhipFy5cmbEiBH2bzphuzne6YDzPL3Oc+GYp98xLwyF0gEAADwihQoAAMAjAioAAACPCKgAAAA8IqACAADwiIAqCj788EPTrVs32zKrWlafMWNGgdO/9tprplOnTubII4+0DaPl5OSYt956y6T6dn/88cfm1FNPNdWqVTPly5c3xx13nHn44YdNqm+3v08++cSUKVPGtGzZ0iSjom77vHnz7HTBn02bNplUP+b79+83d911l6lXr56tEVa/fn3zzDPPmFTe7quuuirk8T7hhBNMqh/v//u//zMtWrQwhx9+uKlVq5b5y1/+YrZv325SfbsnTpxojj/+eHtPP/bYY83zzz9v0hUBVRTs3bvXXkg6sSI9aRVQzZ492yxcuNCcddZZ9iT+6quvTCpvd4UKFcwNN9xgt3/ZsmXm7rvvtp8nnnjCpPJ2u3bt2mX69OljzjnnHJOsirvty5cvNxs3bvR9atSoYVJ9uy+//HIzd+5c8/TTT9vtf+mll+wDJ5W3+5FHHgk4zupqq2rVquayyy4zqbzdelHStX311Veb7777zvz73/82CxYsMP379zepvN2TJ082w4YNMyNHjrTbPWrUKHP99debWbNmmbTkIKq0S6dPn17k3zVt2tQZNWpU2m33RRdd5PTu3dtJh+3u0aOHc/fddzsjRoxwWrRo4SS7SLb9/ffft9Pt3LnTSRWRbPebb77pZGVlOdu3b3dSRXGucU2fkZHh/PTTT04qb/eDDz7oHHPMMQHDHn30UadOnTpOKm93Tk6OM2TIkIBhgwcPdk499VQnHZFClSCdMaszR73JpROlyH366afmjDPOMKnu2WefNT/++KNt3DUdKYtT2SBKmdXbfKp7/fXXTZs2bczYsWNNnTp1TJMmTcyQIUPML7/8YtKJUuc6duxosz1TmYptKDVOuQ6KRTZv3mxeeeUVc95555lUpmztzMzMgGHly5e3qXO//fabSTcEVAlg3LhxZs+ePTaLIB2o42mVKdEDR8nD11xzjUllK1asMEOHDjVTp0615afSiYKoxx57zLz66qv2U7duXXPmmWeaRYsWmVSm4FllBpcsWWKmT59uxo8fbx+wAwcONOliw4YN5s0330z561tUNlRlqHr06GHKli1rsrOzbQe9Rc0aTzadO3c2Tz31lC264jiO+fLLL+13BVPbtm0z6Sa97u4J6MUXX7T5zjNnzky6ciXF9dFHH9kA8rPPPrOBRqNGjUyvXr1MKjpw4IC54oor7DFWKkW6UZkh/3JDp5xyilm1apWtjPDCCy+YVE51VqFePWT1YJWHHnrIXHrppWbSpEn2LT7VPffcc6ZKlSqme/fuJtUtXbrU3HTTTWb48OE2yFD5sdtuu81cd911NpUuVd1zzz22gsnJJ59sA6qaNWuavn372pTZUqXSL72GgCqOXn75Zfv2pgKMShZPFw0aNLB/TzzxRJs0rgKNqRpQKStXb23K3lSBfPdhq5uPUqvefvttc/bZZ5t00q5dO5t6k+opc8rqc4MpUU0oHfd169aZxo0bm1Sm7VSNxiuvvNKm2KS6MWPG2FQqBVHSvHlzWwnntNNOM/fee689H1KRXgx0nB9//HF7L9d2qpJRpUqVbC32dENAFSeq8aNqtQqqzj//fJOuFFwoHz5VqVmMb7/9NmCYUijee+89mwXkBpfpZPHixSn7gHHp4aoXJaXEVqxY0Q774Ycf7Fu7srxT3QcffGBWrlxpa72lg3379h2SnV+6dGn797/lu1PbYYcd5juv9Uy74IILSKFC8eimqZuHa/Xq1fahoULmRx99tK1Wun79el/7HMrmU7Koqhi3b9/e1yaPon3/N9pU226VJ9BwtT8laj5B5cduvPFGk0yKst16gDZr1izg98raVUHO4OGpeMxVdkhBo9ohys/Pt+UrFEwqZS6Vt1vZvKNHjzb9+vWz2b0qT6LUC71EJVN2X1G326VsLt3bkvEcL852q9kbNZGgZgTcLL+bb77ZpsaqTadU3W69JKgAuo71zp07bba2yg0quzctxbuaYSpwq4YHf/r27WvH6+8ZZ5zhm17/L2j6VN1uVSM+4YQTnMMPP9ypXLmyc9JJJzmTJk1yDhw44KTydgdL5mYTirrtDzzwgNOwYUMnMzPTqVq1qnPmmWc67733npNsinPMly1b5nTs2NEpX768c9RRR9nq5Pv27XNSfbt37dplt/mJJ55wklVxtlv3NzV/o22vVauW8+c//9lZt26dk8rbvXTpUqdly5Z2mytXruxceOGFzvfff++kqwz9E++gDgAAIJmlXzF8AACAKCOgAgAA8IiACgAAwCMCKgAAAI8IqAAAADwioAIAAPCIgAoAAMAjAioAJWbKlCm2g9x0pk6SZ8yYEdNl/vTTT3a5auXai/r169vW7hNt+4BEREAFJIGrrrrKPrj0Ub9Z6tW9U6dOtmNS9YdYFOqMumXLllFfx1AP3x49etjuKUramWee6ds/+mj/XHbZZebnn3+O2TKDPxoPIH0QUAFJokuXLraPMKU+vPnmm+ass84yN910k+2I9PfffzeJSP3Wqe/CWFBfato/GzZsMDNnzjRr1641vXv3LrHlvfbaa3Z5+qg/M3n33Xd9wzS+ONR5RaIeTwDhEVABSaJcuXImOzvb1KlTx7Rq1crceeedNnBQcKWsNdeuXbvMNddcY4488khTuXJlc/bZZ5uvv/7ajtN06qxX392UFPe3Bf3ONWvWLNO2bVvbuXP16tXNRRddZIcrNUapQbfccotvvuGy/NSBbMOGDU3ZsmXNsccea1544YWA8fqtOlHWvA8//HDTuHFj8/rrrxe6fzSt9k+tWrXMySefbG644QazaNGigGk++OAD22Gt9qWmGzp0qC94UYevFStWNCtWrPBNP3DgQNuZ9759+w5ZnjqM1fL00T6TatWq+YZpvEudI4fbnnnz5tlt1nFs3bq1XbePP/7YpjyOGTPGdi6twLRFixbmlVde8f1OndH++c9/tsvWeM332WefDVjHH3/80QbeWq5+P3/+/IDxr776qu24WstUCuM//vGPAvex9s3pp59uj3/Tpk3NO++8U+hxAdJGvDsTBFA4dUqqjkdDUUfLXbt29X1Xh7zdunVzvvjiC+eHH35wbr31VqdatWrO9u3bbee8+q5Oqjdu3Gg/boe9Bf1O3njjDad06dLO8OHDbaeoixcvdv7+97/bcZpGHQD/7W9/881Xnn32WScrK8u3bq+99ppz2GGHORMnTnSWL1/u/OMf/7Dz9O8wWbclzevFF190VqxY4dx4441OxYoVfesRijpsvemmm3zfNa225ayzzvINU0e16ph74MCBtuPi6dOnO9WrV7edVbsuu+wyp23bts5vv/1mt1fr+uWXXxZ6fFavXm3X+6uvvjpkXGHb43ZI27x5c+ftt992Vq5cacfde++9znHHHefMmTPHWbVqld2X5cqVc+bNm2d/d/3119uOaXW8tPx33nnHef311wPWR7/XdmhfX3rppU69evXstom2q1SpUvaYabzmr05u9del6R9++GH7f3Vi3qxZM+ecc86xx/6DDz6wHZxrOdqXQLojoAKSPKDq0aOHc/zxx9v/f/TRR7bX9/z8/IBpGjZs6Dz++OP2/wogFIT5i+R3OTk5zp///Oew6+j/8HUFB1SnnHKK079//4BpFMScd955vu96QN99992+73v27LHD3nzzzQIDKgU/FSpUsEGTpm/SpIkNLFx33nmnc+yxxzoHDx70DVNgp+BGwYLs2LHDBj8DBgxwatas6dx3331OJAoLqAraHjegmjFjhm8aHQdtx6effhowr6uvvtrp1auX/b8Cxn79+hW4Pk899ZRv2HfffWeHKZiUK664wunUqVPA72677TanadOmIY/pW2+95ZQpU8ZZv369b7y2gYAK+C+y/IAkp2e2m8WmLLo9e/bYrCdlX7mf1atXm1WrVoWdRyS/U42xc845x9O6Llu2zJx66qkBw/Rdw/01b97c9/8KFSrYLMgtW7YUOG9lf2kdtS3KMmvUqJE599xzze7du33LzsnJ8e0rd9na7nXr1tnvRxxxhHn66ad92ZLKEoyGSLanTZs2vv+vXLnSZjOq4oH/8VC2pHs8BgwYYF5++WVbweD22283n376aYHLVRanuMsNdyyUrXfgwIFD5qXp69ata2rXru0bpv0J4L/K/PEXQJLSg07lbETBgR6cKpcTrKDmCyL5ncrpxIpqMvpTEFRYbcasrCwbRIn+KjDSNk2bNs2WDYvUhx9+aEqXLm0Llu/du9dUqlTJxGJ7FGj5Hw/5z3/+Y8vM+VN5J+natasttzZ79mxblknB7vXXX2/GjRsXcrluIFnUWqEAIkMKFZDE3nvvPfPtt9+aSy65xH5XYfVNmzaZMmXK2KDC/6NC5KLC4MEpEJH8Tqkdc+fODbsuoeYb7PjjjzeffPJJwDB9VwHnaFNQJL/88otv2SqU/d9cuP8tWwHTUUcdZb8rleeBBx6whe+VIqSC7fGg/aHAac2aNYccD6USuVQgvW/fvmbq1Km2yYonnngi4mWEOxZNmjTx7bvg6VVzUoGm67PPPiv2NgKphhQqIEns37/fBj0KWjZv3mzmzJlja4Gp2YQ+ffrYaTp27GizYbp3727Gjh1rH45qRkApHaplpmwl1eZSVp6yxxRIKKCI5HcjRoywqSDKCuvZs6etHafUkTvuuMMuW/NV6o7GKRhwAzF/t912m7n88svNSSedZJepwEXNC6i5Aa+URab9I9o/o0ePtrXRlO3n1thT0DFo0CAbKC1fvtxu0+DBg02pUqVs1uCVV15pbrzxRpv6o32jGo3dunUzl156qYklHZMhQ4bYWpNKUerQoYPJzc21AY+yCxVEDR8+3NYKVC09nRtvvPGGDXoideutt9rt035Se2EKNidMmGAmTZoUcnodL50XWvaDDz5o8vLyzF133RXFrQaS3B9lqQAkeKF0Xa76qGDwkUceaWvlPfPMM74C1a68vDxn0KBBTu3atW1B7bp169rC5GvWrPEVeL7kkkucKlWq2Pm5tboK+528+uqrtmZZ2bJlbQ25iy++2Ddu/vz5tqaaaqK5t5bgQukyadIk55hjjrHLUMHx559/PmB8qELOmod/7bNQhdLd/aPPEUccYYf51x4U1ZBTLT6tf3Z2tnPHHXf4ar2pgPeJJ54YUDBftRCrVq1qawh6KZRe0Pa4hdJ37twZMI0Kz48fP94WpNe+0jHv3LmzrV0no0ePtpURVDNP66hKCz/++GPY9dH8NUzLc73yyiu2ELrmf/TRRzsPPvhggRUNVBuwQ4cOdv/p2KkGIoXSgf/K0D/xDuoAAACSGWWoAAAAPCKgAgAA8IiACgAAwCMCKgAAAI8IqAAAADwioAIAAPCIgAoAAMAjAioAAACPCKgAAAA8IqACAADwiIAKAADAIwIqAAAA483/A4hG+UwcEUFMAAAAAElFTkSuQmCC",
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+       "
"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "
"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "plt.scatter(df[\"config/text_det_box_thresh\"], df[\"WER\"])\n",
+    "plt.xlabel(\"Detection Box Threshold\")\n",
+    "plt.ylabel(\"CER\")\n",
+    "plt.title(\"Effect of Detection Threshold on Word Error Rate\")\n",
+    "plt.show()\n",
+    "\n",
+    "plt.scatter(df[\"config/textline_orientation\"], df[\"WER\"])\n",
+    "plt.xlabel(\"Line Tolerance\")\n",
+    "plt.ylabel(\"WER\")\n",
+    "plt.title(\"Effect of Line Tolerance on Word Error Rate\")\n",
+    "plt.show()\n",
+    "\n",
     "plt.scatter(df[\"config/line_tolerance\"], df[\"WER\"])\n",
     "plt.xlabel(\"Line Tolerance\")\n",
     "plt.ylabel(\"WER\")\n",
     "plt.title(\"Effect of Line Tolerance on Word Error Rate\")\n",
+    "plt.show()\n",
+    "\n",
+    "plt.scatter(df[\"config/text_det_unclip_ratio\"], df[\"WER\"])\n",
+    "plt.xlabel(\"Detection Box Threshold\")\n",
+    "plt.ylabel(\"CER\")\n",
+    "plt.title(\"Effect of Text detection expansion coefficient on Word Error Rate\")\n",
+    "plt.show()\n",
+    "\n",
+    "plt.scatter(df[\"config/text_rec_score_thresh\"], df[\"WER\"])\n",
+    "plt.xlabel(\"Line Tolerance\")\n",
+    "plt.ylabel(\"WER\")\n",
+    "plt.title(\"Effect of Text recognition threshold on Word Error Rate\")\n",
     "plt.show()\n"
    ]
+  },
+  {
+   "attachments": {
+    "image-2.png": {
+     "image/png": 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"
+    },
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DwWD3mgMzIzo6mm8+ZCWu9VooYHYvIQYFBQkvIcpIjhC+MO0qAdu/fz8hZM2aNcLOHaXPnj0bERHBnyOUzl9CAVOMNV+BueQSYvfu3YWXEEVzsYyA/fzzzy1btuzevbvwM76IgPRtJRSw7OxsQghfb1FK165dKy9g8k3kLyHOmDFDtDZVPOGl73qRg64SMPmg10/A2FKvVatWbFdXfn4+u+jNXWDbcLiA1YMP70rVBARMGa/0nSZqIz2VRZcQX331VUKI6H7v5cuXf/31V8VNHG3bth03bpxoRGGW3ePlT0f++uuvv/nNbxq4iYM/B8ZvJu/atYsQ8sILLwiH/vXXX/kt8YEDB/bp00d41LVeCwWMbeK4//77+V2uVatWiTZxeFDAbDbbbbfd1rlz52vXrgmBsBtI1dXVfJ8COzpgwADhxU8ZyxVjzQWMbcmr6yaO3r17810SmzZtEm3icF7AKKVLliwhhMydO5fHiDm7YcMGR8+BCQXsww8/JITwy4lVVVV9+/aVFzD5JjabTWYTR0JCQu33wPGTmVKqeMJL3/XCWLO0M5cQhWtQpiuiE0A+6PUWMBZftvv/8OHDhBD++E1NTc2YMWOEK7B68JHSUKMEAqZMlU2d0m/i4LcupKeySMB+/vnnO+64o2nTpk8//fTq1auXLVvG99ey4dmmoFGjRr355pspKSkTJkxg2+jZjfdmzZotX748MzNTeMOA28122RqNxrlz56amprLdWWwbPasjNY+35RX4YwCrV6/m38Qxd+5cYc1nnnmGEDJq1KiUlJS0tLTZs2eHh4fzS2fPPvusj4/PokWLMjMz2a0U13otFLBaq9jH/xEjRqSlpc2cOVO6jV40CwjXMUKnhGlXrcBqp6GdO3f6+flFRkYmJibWft9BYmLiPffc88ADD1BKL1++zO5rvvHGG2vXrp04cSIhhF/MkV+BKcZaKGCHDh1iNixZsuS1115zchv9wIEDV6xY8cILLwQEBIi20ddJwGw2Gzur77jjjqSkpHXr1iUlJVksFkIIexpE+rlQKGAXLlxo2bKl2Wxevnz5G2+80a9fvz59+sgLmGKTLVu2+Pr61m5nePXVV9esWTNnzpwRI0awE2Djxo21d1UnT578zjvvZGZmskL5E17xbcVWYNJv4tiwYQNb90gJ2BUw+aDXW8AopdHR0R06dPjlxuu2224LDQ1dvHhxamrqvffey2jzFVg9+AjfWeqlIWDKbNl5Jry3ydI8utJTWSRglNJr16698MILt99+u9FoDA0Nveuuu5YtW8bviFRXVy9durRbt27sydZRo0bxJ1u//fbbe+65h22IcHQt8ezZs08++WRoaKjRaOzVqxc3jPkmNU/kM9+h5OPj06JFi549e8bFxfGHloSV165de+edd/r7+zdv3rxXr17PP//86dOnWYWysrIxY8Y0b968dpbh1xJd6LVIwCilaWlp3bp18/X1bdu27fTp04Ufn+VlQOiRMO1CAaOU5ufn134QadWqlclkMpvNEydOZLpeVVU1f/78Pn36NG/evFmzZn369BF+3Ymi5fKxFgoYpXT37t133nknexC4djEtc9GPcfjXv/7Vr18/k8kUEhJi90FmIS7F3mqnyH//+98jRowICQlp2rRpu3btHnnkEb6okk7fQgGjlO7Zs2fQoEHsOfTnn3+e3VeTuQem2IRS+sUXXwwfPpyRr300k39MrK6unjlzZuvWrX18fITXEmVOeMW3ld1HpNnUwZ4GkRKwK2Ds6XVHb/CGCJjwRldhYeGwYcMCAwNDQ0Pj4uIOHTokXIHVg4/wVFEvDQFTjy16BgEQAAEQUJEABExFuOgaBEAABEBAPQIQMPXYomcQAAEQAAEVCUDAVISLrkEABEAABNQjAAFTjy16BgEQAAEQUJEABExFuOgaBEAABEBAPQIQMPXYomcQAAEQAAEVCTQ2AbPZbCUlJVeuXCnHCwRAAARAQM8Erly5UlJSIvypDZEYNjYBKykpkT5xjBIQAAEQAAGdEigpKRHpFs82NgG7cuUKIaSkpETPHztgOwiAAAiAQDlbkIi+O5Sr1/9+bbow0wjS5eXlhBD+o92NwCO4AAIgAALeSUBxPoeAeeeJAa9BAARAQOsEIGBajxDsAwEQAAEQsEsAAmYXCwpBAARAAAS0TgACpvUIwT4QAAEQAAG7BCBgdrGgEARAAARAQOsEIGBajxDsAwEQAAEQsEsAAmYXCwpBAARAAAS0TgACpvUIwT4QAAEQAAG7BCBgdrGgEARAAARAQOsEIGBajxDsAwEQAIHGR6DaVpNXdCEnvzSv6EK1raZ+DkLA6scNrUAABEAABOpJYPOR04OSdpgTctnfoKQdm4+crkdfELB6QEMTEAABEACBehLYfOR01E3pYgIWlZAblZBbDw2DgNUzBmgGAiAAAiBQVwLVthrh2osvwqIScgcl7ajrtUQIWF35oz4IgAAIgEA9CeQVXeCiJU3kFV2oU78QsDrhQmUQAAEQAIH6E8jJL5XqFi/JyS+tU9cQsDrhQmUQAAEQAIH6E8AKrP7sKKWKit2g3tEYBEAABEDAMQF2D0y0icN8YxMH7oE5xnbzCATsJgn8DwIgAAIeIMB2IQo1DLsQnQ0DBMxZUqgHAiAAAuoQwHNg9eQKAasnODQDARDQLQGXfO2Fa713iUmK8zlxrdEe703RYY9bCANAAARAwIUEXLXccaFJrupKcT6HgLkKNfoBARAAAXcTcOHXXrjbdCfGg4A5AQlVQAAEQECHBFz7tRcaBAAB02BQYBIIgAAIuICAax+6coFBru4CAuZqougPBEAABLRBwLVfe6ENn26xAgJ2Cw5kQAAEQKDREMAKDJs4Gs3JDEdAAAS8i4Brv/ZCg+ywAtNgUGASCIAACLiGgAu/9sI1Brm0FwiYS3GiMxAAARDQGAE8B6axgDTAHEXFbkDfaAoCIAACWiTgkq+90KBjivM57oFpMGowCQRAAARAQPnXRSBgOEtAAARAAAS0SAArMC1GBTaBAAiAAAgoEoCAKSJCBRAAARAAAS0SgIBpMSqwCQRAAARAQJEABEwRESqAAAiAAAhokQAETItRgU0gAAIgAAKKBCBgiohQAQRAAARAQIsENCFgaWlpZrPZZDJZLJZ9+/ZJOa1du3bIkCHBN15Dhw4V1qmpqXn55ZfDwsL8/PyGDh164sQJaXNhiaLDwspIgwAIgAAIaJaA4nyu+nNgWVlZRqNx3bp1BQUFcXFxwcHBZ8+eFfF6/PHHV65cmZ+ff+zYsSeeeCIoKKi0tJTVWbJkSVBQUE5OzqFDh2JiYjp27Hj9+nVRc2FW0WFhZaRBAARAAAQ0S0BxPlddwCwWS3x8PANks9nCw8OTk5NleFVXVzdv3nz9+vWU0pqamrCwsKVLl7L6V65cMZlMmZmZMs0VHZZpi0MgAAIgAALaIaA4n6srYFVVVQaDITs7mxOZMmVKTEwMz0oTV69e9fPz++ijjyil3333HSEkPz+fV7vnnntmzZrFsyxRWVlZfvNVUlJCCCkvLxfVQRYEQAAEQMCFBNzwBYweFrBTp04RQvLy8ji1+fPnWywWnpUmpk+f3qlTJ3adcM+ePYSQ06dP82oPP/zwxIkTeZYlEhMTya0vCJgIEbIgAAIg4EIC7vkKfJ0JWHJycsuWLQ8dOsRAOylgWIG58LxEVyAAAiAgT4D9CJk5IZf/RSXkRiXkbj7y/4sN+R6cPOphAavTJcSlS5cGBQUdOHCA++bkJURen1Llby8WVkYaBEAABECgTgTYz0Bz6eKJqITcQUk7qm01depNvrKHBYxSarFYZsyYway02WwRERF2N3G8/vrrLVq0+PLLL4X+sE0cy5YtY4Xl5eXYxCHkgzQIgAAIuJlAXtEFLlrSRF7RBRfa43kBy8rKMplMGRkZhYWF06ZNCw4OLisrq92gMXny5AULFjBXlyxZYjQa//3vf5+5+bp27Ro/FBwc/MEHHxw+fHjcuHHYRu/CkwNdgQAIgEBdCeTkl0p1i5fk5P/fE1B17dZufc8LGKU0NTU1MjLSaDRaLJa9e/cyQ61Wa2xsLEubzeZbN2GQxMREdog9yNy2bVuTyTR06NDjx4/b9ZMXKjrMayIBAiAAAiBQVwLetQKrK50G1oeANRAgmoMACICADAF2DyxKsIODLb8a5z0wGRBqHIKAqUEVfYIACIAAJ8B2IQo1rHHuQuQOuy0BAXMbagwEAiDgtQS84jkw90cXAuZ+5hgRBEDACwk0/m/icH9QIWDuZ44RQQAEQEANAorzubrfhaiGS/J9Kjos3xxHQQAEQAAENEJAcT6HgGkkUjADBEAABEDgFgIQsFtwIAMCIAACIKAXAhAwvUQKdoIACIAACNxCAAJ2Cw5kQAAEQMAbCLhhi6AbMELA3AAZQ4AACICAhgi45yEtNzgMAXMDZAwBAiAAAloh4LYf63KDwxAwN0DGECAAAiCgCQLu/LEuNzgMAXMDZAwBAiAAApog4M6vineDwxAwN0DGECAAAiCgCQLu/LEuNzgMAXMDZAwBAiAAApoggBWYJsJQbyMUFbvePaMhCIAACGicgDt/rMsNKBTnc3yVlBuigCFAAARAwE0E3PZjXW7wBwLmBsgYAgRAAAQ0RADPgWkoGHUyRVGx69QbKoMACICAHgngmzj0GDUKAdNl2GA0CIAACEgIKM7nuAcmYYYCEAABEGiMBHS3LIOANcbTED6BAAiAQB0J6PHGGASsjkFGdRAAARBodAR0+gWJELBGdybCIRAAARCoCwH9fkEiBKwucUZdEAABEGh0BPT79RwQsEZ3MsIhEAABEKgLAf1+QSIErC5xRl0QAAEQaHQEsALTTUgVFVs3nsBQEAABEHAFAf1+QaLifI7nwFxxgqAPEAABENAwAZ1+QSIETMPnFEwDARAAAXcRwHNg7iLdgHEUFbsBfaMpCIAACOiYAL6JQ+vBg4BpPUKwDwRAAAScI6A4n+MemHMgUQsEQAAEQMC9BCBg7uWN0UAABEAABFxEwPMClpaWZjabTSaTxWLZt2+f1K+jR49OmDDBbDYTQlJSUoQVqqurX3rppaioKD8/v06dOi1cuLCmpkZYQZpWdFjaBCUgAAIgAAIaJKA4n6t7CTErK8toNK5bt66goCAuLi44OPjs2bMiTPv37583b15mZmZYWJhIwBYvXtyqVavc3Nzi4uL33nsvMDDwzTffFDUXZRUdFtVHFgRAAARAQJsEFOdzdQXMYrHEx8czNDabLTw8PDk52REps9ksErAxY8Y89dRTvP6ECRMmTZrEs3YTig7bbYVCEAABEAABrRFQnM9VFLCqqiqDwZCdnc2hTJkyJSYmhmdFCamALV682Gw2Hz9+nFJ68ODBNm3avPPOO6JWlNLKysrym6+SkhJCSHl5ubQaSkAABEAABHREwJMCdurUKUJIXl4e5zV//nyLxcKzooRUwGw2W0JCgo+PT9OmTX18fJKSkkRNWDYxMZHc+oKA2QWFQhAAARDQEQF9C1hmZmb79u0zMzMPHz789ttvh4SEZGRkSOljBSZlghIQAAEQ0DsBTwpYwy8htm/fPi0tjcdg0aJFXbt25Vm7CUWH7bZCIQiAAAiAgNYIKM7nKt4Do5RaLJYZM2YwKDabLSIiok6bOEJCQlatWsWZJiUlde7cmWftJhQdttsKhSAAAiAAAlojoDifqytgWVlZJpMpIyOjsLBw2rRpwcHBZWVllNLJkycvWLCAwaqqqsq/8WrXrt28efPy8/NPnjzJDsXGxkZERLBt9O+//35oaOjzzz8vj1jRYfnmOAoCIAACIKARAorzuboCRilNTU2NjIw0Go0Wi2Xv3r2Mi9VqjY2NZeni4uJbd2AQq9XKDl29enX27NmRkZHsQeYXX3yxqqpKnqyiw/LNcRQEQAAEQEAjBBTnc9UFzM0gFB12sz0YDgRAAARAoH4EFOdzCFj9wKIVCIAACICAugQgYOryRe8gAAILU/HjAAAgAElEQVQgAAIqEYCAqQQW3YIACIAACKhLAAKmLl/0DgIgAAIgoBIBCJhKYNEtCIAACICAugQgYOryRe8gAAIgAAIqEYCAqQQW3YIACIAACKhLAAKmLl/0DgIgAAIgoBIBCJhKYNEtCIAACICAugQgYOryRe8gAAIgAAIqEYCAqQQW3YIACIAACKhLAAKmLl/0DgIgAAIgoBIBCJhKYNEtCIAACICAugQgYOryRe8gAAIgAAIqEYCAqQQW3YIACIAACKhLAAKmLl/0DgIgAAIgoBIBCJhKYNEtCIAACICAugQgYOryRe8gAAIgAAIqEYCAqQQW3YIACIAACKhLAAKmLl/0DgIgAAIgoBIBCJhKYNEtCIAACICAugQgYOryRe8gAAIgAAIqEYCAqQQW3YIACIAACKhLAAKmLl/0DgIgAAIgoBIBCJhKYNEtCIAACICAugQgYOryRe8gAAIgAAIqEYCAqQQW3YIACIAACKhLAAKmLl/0DgIgAAIgoBIBCJhKYNEtCIAACICAugQgYOryRe8gAAIgAAIqEYCAqQQW3YIACIAACKhLAAKmLl/0DgIgAAIgoBIBCJhKYNEtCIAACICAugQgYOryRe8gAAIgAAIqEfC8gKWlpZnNZpPJZLFY9u3bJ/Xz6NGjEyZMMJvNhJCUlBRRhdLS0kmTJoWEhPj5+UVHRx84cEBUQZRVdFhUH1kQAAEQAAFtElCcz4mqdmdlZRmNxnXr1hUUFMTFxQUHB589e1Y04v79++fNm5eZmRkWFiYSsEuXLpnN5ieeeGLfvn3ff//91q1bi4qKRM1FWUWHRfWRBQEQAAEQ0CYBxflcXQGzWCzx8fEMjc1mCw8PT05OdkTKbDaLBCwhIWHIkCGO6tstV3TYbisUggAIgAAIaI2A4nyuooBVVVUZDIbs7GwOZcqUKTExMTwrSkgFrHv37s8999xDDz3UunXrvn37rl27VtSEZSsrK8tvvkpKSggh5eXldmuiEARAAARAQC8EPClgp06dIoTk5eVxWPPnz7dYLDwrSkgFzHTj9cILL3zzzTdr1qzx8/PLyMgQtarNJiYmkltfEDApJZSAAAiAgL4I6FvAfH19Bw8ezInPnDlz0KBBPMsTWIFxFEiAAAiAQKMh4EkBa/glxMjIyKlTp/JgrFq1Kjw8nGftJhQdttsKhSAAAiAAAlojoDifq3gPjFJqsVhmzJjBoNhstoiIiDpt4njssceEmziee+454YLMLmtFh+22QiEIgAAIgIDWCCjO5+oKWFZWlslkysjIKCwsnDZtWnBwcFlZGaV08uTJCxYsYLCqqqryb7zatWs3b968/Pz8kydPskP79+9v2rTp4sWLT548+c9//jMgIOCdd96RR6zosHxzHAUBEGgEBKptNXlFF3LyS/OKLlTbahqBR97pguJ8rq6AUUpTU1MjIyONRqPFYtm7dy8Lg9VqjY2NZeni4uJbd2AQq9XKo/XRRx9FR0ebTKZu3bo52oXIK1NKFR0WVkYaBECg8RHYfOT0oKQd5oRc9jcoacfmI6cbn5ve4JHifK66gLmZsqLDbrYHw4EACLiTwOYjp6NuShcTsKiE3KiEXGiYO6PgqrEU53MImKtQox8QAAEPE6i21QjXXnwRFpWQOyhpB64lejg8dR8eAlZ3ZmgBAiCgTwJ5RRe4aEkTeUUX9OmW91oNAfPe2MNzEPA2Ajn5pVLd4iU5+aXeBkTv/kLA9B5B2A8CIOAsAazAnCWlk3oQMJ0ECmaCAAg0mAC7BybaxGG+sYlD7/fAvPPBAAhYg98T6AAEQEA/BNguRKGGNYJdiF77YAAETD/vPFgKAiDgCgKNbLr35gcDIGCueEOgDxAAAV0RaDQX3Lz8wQAImK7edjAWBEAABAQEvHxbCgRMcC4gCQIgAAK6IuDlDwZAwHR1tsJYEAABEBAQwAqMECLzA8X4KinByYIkCIAACGiJQCN+MMAZzFiBOUMJdUAABEBAowQa5YMBTrKGgDkJCtVAAARAQKMEGtmDAc5ThoA5zwo1QQAEQECjBBrNgwF14gsBqxMuVAYBEAABENAKAQiYViIBO0AABEAABOpEAAJWJ1yoDAIgAAIgoBUCEDCtRAJ2gAAIgAAI1IkABKxOuFAZBEAABEBAKwQgYFqJBOwAARAAARCoEwEIWJ1woTIIgAAIgIBWCEDAtBIJ2AECIAACIFAnAhCwOuFCZRAAARAAAa0QgIBpJRKwAwRAAARAoE4EIGB1woXKIAACIOA+At75BVHO84WAOc8KNUEABEDAfQS89it6nUcMAXOeFWqCAAiAgJsIsB9JMSfk8r+ohNyohNzNR067yQI9DAMB00OUYCMIgIA3EWA/U8mliyeiEnIHJe2ottV4Eww5XyFgcnRwDARAoDER0MstpbyiC1y0pIm8oguNKSgN8QUC1hB6aAsCIKAbAjq6pZSTXyrVLV6Sk1+qG+gqG6qigF2/fn3p0qUq21/n7hUdrnOPaAACIKB5Avq6pYQVmJMnlOJ8Tpzp6Ny5cx999NHWrVurq6sppb/88suKFSvatm3bqlUrZ5q7s46iw+40BmOBAAi4gYDubikxg6MEOzjY8gv3wERni+J8rixgn3/+eVBQkI+PT5MmTSwWS0FBQefOnbt377569eqKigrReB7PKjrscQthAAiAgGsJ6HFBw5aMQg3DLkTpWaE4nysLmNVqfeyxx44cOTJv3jwfH58uXbq899570pFkStLS0sxms8lkslgs+/btk9Y8evTohAkTzGYzISQlJUVagVKanJxMCJk9e7bdo7xQ0WFeEwkQAIHGQUCnt5R0dNPOU+eJ4nyuLGAhISEFBQWU0oqKiiZNmuTk5NTJmaysLKPRuG7duoKCgri4uODg4LNnz4p62L9//7x58zIzM8PCwuwK2P79+6Oionr37u21AqaX7VWiyCILAm4goMcVGMOC97X86eECAfPx8eGSExgYWFRUJD+k6KjFYomPj2eFNpstPDw8OTlZVIdnzWazVMCuXbvWuXPn7du3W61W7xQwfFLjZwgSICAlgFtKUiaNo8Q1ArZz585DN17NmjX7+OOPWZr9K4+pqqrKYDBkZ2fzalOmTImJieFZUcKugE2ZMuW5556jlDoSsMrKyvKbr5KSEkJIeXm5qGf9ZvW1vUq/nGG5rgnglpKuw+fIeNcIWJMmTXxufbGSJk2aOBqYlZ86dYoQkpeXx6vNnz/fYrHwrCghFbDMzMzo6Ojr16/LCFhiYiK59dVoBEx326tEAUUWBNxGABcq3IbabQO5QMD+K/uS96SBAvbjjz+2adPm0KFDbBQvXIHp9+K+/ImBoyCgBgHcUlKDqgf7dIGANcT6Bl5CzM7OJoQYbr4IIT4+PgaDgT2OZtcwRYftttJsoU63V2mWJwwDARDQEQHF+Vx5F+Lrr7/On/f64osvKisrmf9Xr16dPn26IguLxTJjxgxWzWazRUREOL+J4+rVq0cEr/79+//+978/cuSIzKCKDsu01eAhrMA0GBSYBAIg4B4CivO5soA1adKE70Js3rz5d999x0wvKytTvAdGKc3KyjKZTBkZGYWFhdOmTQsODi4rK6OUTp48ecGCBayrqqqq/Buvdu3azZs3Lz8//+TJk1JAji4hCmsqOiysrP00tldpP0awUG0CuDCoNmHN9q84nysLmGgbfV0FjFKampoaGRlpNBotFsvevXsZLKvVGhsby9LFxcW3bsIgVqtVytQLBYxSiu1V0jMBJd5DAFszvCfWUk81IWBSs9QrUXRYvaHV6xnvYfXYomctE8AzJFqOjhtsU5zP3bECc4OffAhFh3lNfSVwFUVf8YK1DSeAZ0gazlDvPSjO504J2OLFi9+88fLz83v55ZdZ+rXXXnPmHpibCSo67GZ7MBwIgED9CGAHU/24NaZWivO5soCZzeYoxy+twVJ0WGsGwx4QAAG7BPAMiV0sXlWoOJ8rC5i+eCk6rC93YC0IeC0BrMC8NvTcccX5XFnAPvnkk+7du4u+nOnKlSs9evT47LPP+EgaSSg6rBE7YQYIgIA8ATxDIs/HG44qzufKAjZ27Ng33nhDCuvNN98cP368tNyzJYoOe9Y8jA4CIOA8ATxD4jyrRllTcT5XFrDIyMjCwkIpnWPHjnXo0EFa7tkSRYc9ax5GBwEQqBMBPENSJ1yNrLLifK4sYCaTye73Ypw8edLPz09rvBQd1prBsAcEQECeAJ4hkefTiI8qzufKAtapUyfhD3pxWP/5z386duzIsxpJKDqsETthBgiAAAiAgDwBxflcWcBmzJjBf5GLD1ZRUREdHT1z5kxeopGEosMasRNmgAAIgAAIyBNQnM+VBaysrCw8PLxDhw6vv/56zo3XkiVLOnToEB4ezr6WV94CNx9VdNjN9mA4EAABEACB+hFQnM+VBYxS+t///nfUqFH8d5mbNGkyatSo77//vn42qdpK0WFVR0fnIAACIAACriKgOJ87JWDMmkuXLu3fv3/fvn2XLl1ylX0u70fRYZePiA5BAARAAATUIKA4n9dBwNSwz+V9Kjrs8hHRIQiAAAiAgBoEFOdzCJga2NEnCIAACIBAQwlAwBpKEO1BAARAAAQ8QgAC5hHsGNQFBPD4qgsgogsQ0DMBCJieo+fFtuMLhLw4+HAdBP6PAAQMp4L+COCH5PUXM1gMAioQgICpABVdqkkAPySvJl307WECuDBepwBAwOqEC5U9T0CDP2OIScfzp0WjsAAXxusaRghYXYmhvocJaO2H5DHpePiE0P/w7APQnz88ak7IFf5FJeRGJeRuPnJa/y6q5QEETC2y6FclAppageFunEpR9p5uRR+AhAJmviFgg5J2VNtqvAdInTyFgNUJFyp7noB2fkged+M8fzbo3ALpByCRgLFsXtEFnTuqlvkQMLXIol/1CGjkh+Q1tRZUjzZ6VomAow9AUg3LyS9VyQa9dwsB03sEvdR+0YWXQUk73H+rQGt347z0VNCt2/IfgIQyhhWYoyBDwByRQbnWCXh875/8BIRJR+snkKftk/8AxAQsKiEX98BkAgUBk4GDQyAgR0A7d+PkrMQxrRKQ/wDEdnBgF6J89CBg8nxwFATkCGjkbpyciTimVQKOPgDxi4ceuTCuVVr27YKA2eeCUhBwkoAW7sY5aSqqaY2A9AMQU6+FHx7NK7qA3fOK8YKAKSJCBRBQIODxu3EK9uGwhgngA1BDggMBawg9tAUBEACBhhLAB6B6E4SA1RsdGoIACIAACHiSgCYELC0tzWw2m0wmi8Wyb98+KY+jR49OmDDBbDYTQlJSUoQVkpKS+vfvHxgY2Lp163Hjxn377bfCo9K0osPSJigBARAAARDQIAHF+ZyobXRWVpbRaFy3bl1BQUFcXFxwcPDZs2dFg+7fv3/evHmZmZlhYWEiARs5cmR6evrRo0cPHjw4evToyMjIn376SdRcmFV0WFgZaRAAARAAAc0SUJzPVRcwi8USHx/PANlstvDw8OTkZEe8zGazSMCENc+dO0cI2b17t7BQlFZ0WFQfWRAAARAAAW0SUJzP1RWwqqoqg8GQnZ3N6UyZMiUmJoZnRQl5ATt58iQh5MiRI6JWwqyiw8LKSIMACIAACGiWgOJ8rq6AnTp1ihCSl5fHAc2fP99isfCsKCEjYDabbcyYMb/5zW9ETSillZWV5TdfJSUlhJDy8nJpNZSAAAiAAAjoiEDjEbA//OEPZrO5pKRESj8xMZHc+oKASSmhBARAAAT0RcDDAuaqS4jx8fHt27f//vvv7dLHCswuFhSCAAiAgK4JeFjAKKUWi2XGjBkMos1mi4iIqNMmjpqamvj4+PDw8BMnTjgTCUWHnekEdUAABEAABDxOQHE+V/ceGKU0KyvLZDJlZGQUFhZOmzYtODi4rKyMUjp58uQFCxYwQFVVVfk3Xu3atZs3b15+fv7JkyfZoenTpwcFBe3atevMzVdFRYUMVkWHZdriEAiAAAiAgHYIKM7nqgsYpTQ1NTUyMtJoNFoslr179zI6Vqs1NjaWpYuLi2+9h0WsVis7JConhKSnp8vwVXRYpi0OgQAIgAAIaIeA4nzuDgFzJw5Fh91pDMYCARAAARCoNwHF+RwCVm+2aAgCIAACIKAiAQiYinDRNQiAAAiAgHoEIGDqsUXPIAACIAACKhKAgKkIF11rigB+dUlT4YAxINBwAhCwhjNEDzoggN+91UGQYCII1JEABKyOwFBdhwQ2HzkdlZBrFvxFJeRGJeRuPnJah97AZBAAgf8jAAHDqdDICVTbagYl7RCqF0tHJeQOStpRbatp5P7DPRBovAQgYI03tvDsBoG8ogtS9eIleUUXwAkEQECnBCBgOg0czHaWQE5+KZcraSInv9TZjlAPBEBAYwQgYBoLCMxxNQGswFxNFP2BgFYIQMC0EgnYoRIBdg9MtInDfGMTB+6BqcRc7W7xRITahPXSPwRML5GCnfUnwHYhCjUMuxDrT9PTLfFEhKcjoKHxIWAaCgZMUY8AZj312LqzZzwR4U7a2h8LAqb9GMFC1xDAdSfXcPRcL3giwnPsNToyBEyjgYFZIAACIgLYjyMCgiwEDOcACICAPgjgiQh9xMmNVkLA3AgbQ4EACDSAAFZgDYDXOJtCwBpnXOEVCDQ+AngiovHFtIEeQcAaCBDNQQAE3EcAT0S4j7UeRoKA6SFKsBEEQOAmATwRcZME/qcQMJwEIAACOiOAJyJ0FjDVzIWAqYYWHYMACIAACKhJAAKmJl30DQIgAAIgoBoBCJhqaNExCIAACICAmgQgYGrSRd8gAAIgAAKqEYCAqYYWHYMACIAACKhJAAKmJl30DQIgAAIgoBoBCJhqaNExCIAACICAmgQgYGrSRd8gAAIgAAKqEYCAqYYWHYMACIAACKhJAAKmJl30DQIgAAIgoBoBCJhqaNExCIAACICAmgQgYGrSRd8gAAIgAAKqEYCAqYYWHYMACIAACKhJQBMClpaWZjabTSaTxWLZt2+f1N+jR49OmDDBbDYTQlJSUkQVFJsL6ys6LKyMNAiAAAiAgGYJKM7nRG3Ts7KyjEbjunXrCgoK4uLigoODz549Kxp0//798+bNy8zMDAsLEwmYM82FvSk6LKyMNAiAAAiAgGYJKM7nqguYxWKJj49ngGw2W3h4eHJysiNeZrNZJGB1ak6p8g+gORoa5W4jgF97chtqDAQCuibgYQGrqqoyGAzZ2dkc4pQpU2JiYnhWlBAJWF2bQ8BEPDWYxe/tajAoMAkEtEnAwwJ26tQpQkheXh6nM3/+fIvFwrOihEjAnGxeWVlZfvNVUlJCCCkvLxf1jKwWCGw+cjoqIdcs+ItKyI1KyN185LQWzIMN7iGAJbh7ODeCUbxCwBITE8mtLwiYBs/dalvNoKQdQvVi6aiE3EFJO6ptNRq0GSa5nACW4C5H2og79LCA1fUaoGgF5mRzrMB0cQbnFV2QqhcvySu6oAsvYGRDCGAJ3hB6XtjWwwJGKbVYLDNmzGDobTZbREREXTdxON8c98C0fIrn5JdyuZImcvJLtWw8bGs4ASzBG87Q23rwvIBlZWWZTKaMjIzCwsJp06YFBweXlZVRSidPnrxgwQIWj6qqqvwbr3bt2s2bNy8/P//kyZPskKPmjgKp6LCjhihXmwBWYGoT1nj/OAE0HiANmqc4n6u+jZ5SmpqaGhkZaTQaLRbL3r17GSar1RobG8vSxcXFt97DIlarldO025wfFSUUHRbVR9ZtBNgHcNEmDvONTRy4B+a2KHhwICzBPQhfp0MrzufuEDB3slN02J3GYCwRAXYLRKhh2IUoQtSIs1iBNeLgquSa4nwOAVOJPLq1TwCb0Oxz8YJSLMG9IMgudhEC5mKg6K7hBPAYUMMZ6rQHLMF1GjhPmQ0B8xR5jAsCIGCHAJbgdqCgyAEBCJgDMCgGARDwEAEswT0EXn/DQsD0FzNYDAIgAAIg4MxzvdjEgfMEBEAABEBAiwSwAtNiVGATCIAACICAIgEImCIiVAABEAABENAiAQiYFqMCm0AABEAABBQJQMAUEaECCIAACICAFglAwLQYFdgEAiAAAiCgSAACpogIFUAABEAABLRIAAKmxajAJhAAARAAAUUCEDBFRKgAAiAAAiCgRQIQMC1GBTaBAAiAAAgoEoCAKSJCBRAAARAAAS0SgIBpMSqwCQRAAARAQJEABEwRESqAAAiAAAhokQAETItRgU0gAAIgAAKKBCBgiohQAQRAAARAQIsEIGBajApsAgEQAAEQUCQAAVNEhAogAAIgAAJaJAAB02JUYBMIgAAIgIAiAQiYIiJUAAEQAAEQ0CIBCJgWowKbQAAEQAAEFAlAwBQRoQIIgAAIgIAWCUDAtBgV2AQCIAACIKBIAAKmiAgVHBKottXkFV3IyS/NK7pQbatxWA8HQAAEQEAFAhAwFaB6R5ebj5welLTDnJDL/gYl7dh85LR3uO55L/HRwfMxgAUaIAAB00AQdGjC5iOno25KFxOwqITcqIRcaJgbgomPDm6AjCF0QQACposwacvIaluNcO3FF2FRCbmDknbgWqKq0cJHB1XxonN9EYCA6StemrA2r+gCFy1pIq/ogiasbIxG4KNDY4wqfKo/AQhY/dl5bcuc/FKpbvGSnPxSryWjtuP46KA2YfSvLwIQMH3FSxPWYhr1VBjw0cFT5DGuNgloQsDS0tLMZrPJZLJYLPv27bNLauPGjV27djWZTNHR0R9//DGvc+3atfj4+IiICD8/v+7du69evZofsptQdNhuKxQKCbALWaJNHOYbmzhwD0wIyuVpfHRwOVJ0qGsCivM5Udu9rKwso9G4bt26goKCuLi44ODgs2fPigbds2ePwWD4y1/+UlhY+NJLL/n6+h45coTViYuLu+2223bu3FlcXLxmzRqDwfDBBx+Imguzig4LKyPtiADbSiDUMOxCdMTKheWO7oGx67cfH8JjDC6Eja50QEBxPlddwCwWS3x8PENls9nCw8OTk5NF5CZOnDhmzBheOHDgwGeeeYZle/bsuXDhQn7ojjvuePHFF3lWmlB0WNoEJXYJYDO3XSxqF3586BS/3ShKYPmrNnz0rzUCivO5ugJWVVVlMBiys7M5lylTpsTExPAsS3To0CElJYUXvvLKK71792bZuLi4/v37l5aW1tTUfPrpp4GBgbt37+Y1pQlFh6VNUOKIAB6ndURGvXJcRVSPLXrWHQHF+VxdATt16hQhJC8vj4ObP3++xWLhWZbw9fV99913eeHKlSvbtGnDspWVlVOmTCGENG3a1Gg0rl+/nlfjicrKyvKbr5KSEkJIeXk5P4oECOiIAPZx6ChYMFVtAroXsKVLl3bp0uXDDz88dOhQampqYGDg9u3bRdQSExPJrS8ImAgRsnohgBWYXiIFO91AwMMC1sBLiBUVFb6+vrm5uZzU1KlTR44cybMsgRWYCAiy+iWALaD6jR0sdzkBDwsYpdRiscyYMYM5ZrPZIiIi7G7ieOCBB7jzgwcPZps4mPWbNm3ih6ZNmzZ8+HCelSYUHZY2QQkIaIoAtoBqKhwwxoMEFOdzde+BUUqzsrJMJlNGRkZhYeG0adOCg4PLysoopZMnT16wYAFDs2fPnqZNmy5btuzYsWOJiYnCbfRWq7Vnz547d+78/vvv09PT/fz8Vq1aJQNU0WGZtjgEAhohgC2gGgkEzPAsAcX5XHUBo5SmpqZGRkYajUaLxbJ3715GxGq1xsbGcjobN27s0qWL0Wjs2bOn8EHmM2fOPPHEE+Hh4X5+fl27dl2+fHlNjdwPUyk6zEdEAgS0TABbQLUcHdjmHgKK87k7BMw9rrJRFB12pzEYCwRAAARAoN4EFOdzCFi92aIhCIAACICAigQgYCrCRdcgAAIgAALqEYCAqccWPYMACIAACKhIAAKmIlx0DQIgAAIgoB4BCJh6bNEzCIAACICAigQgYCrC1WbX2H6tzbjAKhAAgboSgIDVlZi+6+MBWH3HD9aDAAgICEDABDAae5J9BZHwR6TwK5SNPebwDwQaMwEIWGOOrtA3Rz/mG5WQix9CFIJCGgRAQC8EIGB6iVRD7cTPcDSUINqDAAhojAAETGMBUc0c/BCiamjRMQiAgGcIQMA8w939o2IF5n7mGBEEQEBVAhAwVfFqqHP8EKKGggFTQAAEXEEAAuYKijrpAz+EqJNAwUwQAAGnCEDAnMLUaCrhObBGE0o4AgIgAAHzunMA38ThdSGHwyDQSAlAwBppYOEWCKhAAJ9+VICKLutPAAJWf3ZoCQJeRQDXn70q3LpwFgKmizDBSBDwMAF8D5mHA4Dh7RGAgNmjgjIQAAEBAXwPmQAGkhoiAAHTUDBgCghokwCegtdmXGAVBAznAAiAgAIBfA+ZAiAc9hABCJiHwGNYxwSw1c0xG88cwQrMM9wxqhIBCJgSIRx3LwFsdXMvb6dGw/eQOYUJldxOAALmduQY0DEBbHVzzMbDRxr+PWRYWHs4hI1xeAiYzqLaiGcBHW1101cUXGVtQxbHDWmrs7cozHUjAQhYnWG7ajqQH9juKI17FtDLjRZ9RcG11to9LeXPZEopFtaKiFChfgQgYHXj5trpwNHYdkdxNAt8fOh0XtGFnPzSvKIL1bYaR31qv1wXW90cRWHzkdMaJKwFa3W0sNZgBGGSPAEImDyfW466ZzqwO4o5IbfPn7eaE3Klfx0X/H/hoKQd2pxJb+HoIKP9FZi+5mKNWKv9sDo4H1GsAwIQMGeD5J7pwNEoUt2yWxKVkBuVkKtTDWO+R0lEOiohd1DSDi0sLvU1F2vEWl0srJ2dBVBPYwQgYM4GxD3TgfwodkVLVKid6d5ZsoJ6bPUp1DBNSbK+5mKNWCt/SucVXRDEH0kQqBsBCJizvNwzHciPItIqmax+5wW79/+cDZLK9fQ1F2vEWu0vrFU+a9C9iqSBe3sAABVjSURBVAQgYM7Cdc90ID+KjGKJDuXklzrrmPbq1W+rmxv80NdcrB1rNb6wdsOZgyFUIgABcxase6YDmVH6/Hkru54m0iq7Wf2uwJyNh4fq6Wsu1o61Wl5Ye+hUwrAuIKAJAUtLSzObzSaTyWKx7Nu3z65bGzdu7Nq1q8lkio6O/vjjj4V1CgsLx44d26JFi4CAgP79+//www/Co6K0osOi+sKse6YDmVFEs4Bw/yGXMV3fAxPS1mxaFAWN7/zUjrWaXVhr9kyDYYoEFOdzothFAytkZWUZjcZ169YVFBTExcUFBwefPXtW1OeePXsMBsNf/vKXwsLCl156ydfX98iRI6xOUVFRSEjI/Pnzv/nmm6Kiog8++EDaXNibosPCytK0e6YDmVGEs8DHh06J1mSa2vIgpddoSoRR0ML2SHmw+rJW3hccBQEhAcX5XHUBs1gs8fHxzCabzRYeHp6cnCw0kVI6ceLEMWPG8MKBAwc+88wzLPvII4/8/ve/54cUE4oOK/bgnunAyVFkpE7REVQAARAAAV0TUJzP1RWwqqoqg8GQnZ3NIU6ZMiUmJoZnWaJDhw4pKSm88JVXXunduzel1GazBQYGLly4cMSIEa1bt7ZYLMKueH1hQtFhYWVdpJ2UOl34AiNBAARAwHkCivO5ugJ26tQpQkheXh63eP78+RaLhWdZwtfX99133+WFK1eubNOmDaX0zJkzhJCAgIA33ngjPz8/OTnZx8dn165dvCZLVFZWlt98lZSUEELKy8tFdZAFARAAARDQFwF9CxjTv8cee4xDHzt27KOPPsqzLJGYmEhufUHARIiQBQEQAAHdEfCwgDXwEmJVVVXTpk0XLVrEuT///PN33XUXz7IEVmAiIMiCAAiAQCMg4GEBo5RaLJYZM2YwlDabLSIiwu4mjgceeIDjHjx4MN/EMXjwYOEmjvHjxwsXZLwJTyg6zGsiAQIgAAIgoGUCivO5uvfAKKVZWVkmkykjI6OwsHDatGnBwcFlZWWU0smTJy9YsICx27NnT9OmTZctW3bs2LHExEThNvr333/f19d37dq1J0+eTE1NNRgMn3/+uQxxRYdl2uIQCIAACICAdggozueqCxilNDU1NTIy0mg0WiyWvXv3MjpWqzU2NpaT2rhxY5cuXYxGY8+ePUUPMv/jH/+4/fbb/fz8+vTpk5OTw5vYTSg6bLcVCkEABEAABLRGQHE+d4eAuROKosPuNAZjgQAIgAAI1JuA4nwOAas3WzQEARAAARBQkQAETEW46BoEQAAEQEA9AhAw9diiZxAAARAAARUJeJ2AXblyhRBSUlJy86s58D8IgAAIgIAuCbBvVrpy5YojkWxs98CYw7d+LwdyIAACIAACeiVQUlLiLQJms9lKSkquXLmix88bTH29cPnonY57p9fl5eXe6bh3et3AcF+5cqWkpMRms3mLgDnyUxflihd8deFFPYz0Tse902tKqXc67p1eqx3uxnYJsR6zp3aa4BTXTizcYAnC7QbI2hkC4VYjFhAwNajWs0+c4vUEp89mCLc+41ZPqxHueoKTbQYBk8Xj3oOVlZWJiYm1/7p3WM+P5p2Oe6fXlFLvdNw7vVY73BAwz0/fsAAEQAAEQKAeBCBg9YCGJiAAAiAAAp4nAAHzfAxgAQiAAAiAQD0IQMDqAQ1NQAAEQAAEPE8AAub5GMACEAABEACBehCAgNUDWj2bpKWlmc1mk8lksVj27dsn00tmZiYhZNy4cbxObGys8HtgRo4cyQ9pPKHodXp6utA1k8nEPaqpqXn55ZfDwsL8/PyGDh164sQJfkj7iYY43ojDTSm9fPnys88+GxYWZjQaO3fuLPwBW0Vomo27M5Y7cjwxMVH4Fujatatm3RQZpui11WoVukYIGT16NOvEJe9uCJgoImpls7KyjEbjunXrCgoK4uLigoODz549a3ew4uLiiIiIu+++WyRg999//5mbr0uXLtltq7VCZ7xOT09v0aLFTc/OlJWVcS+WLFkSFBSUk5Nz6NChmJiYjh07Xr9+nR/VcqKBjsfGxjbWcFdVVfXv33/06NFffPFFcXHxrl27Dh48yELpDDRtBt0Zy2UcT0xM7NmzJ38LnD9/XptuiqxyxuuLFy9yv44ePWowGNLT01k/Lnl3Q8BEQVEra7FY4uPjWe82my08PDw5OVk6WHV19V133fX3v/89NjZWJGDCrLShNkuc8To9PT0oKEhqf01NTVhY2NKlS9mhK1eumEymzMxMaU0NljTEcUqpKPoadNCuSc54vXr16k6dOv3yyy/SHpxpLm2lhRJnLJdxPDExsU+fPlpwpE42OOO1sMOUlJTmzZv/9NNPlFJXvbshYELCaqWrqqoMBkN2djYfYMqUKTExMTzLE6+88sr48eOlU1hsbGxQUFDr1q27dOnyhz/84cKFC7yJZhNOep2enm4wGCIjI9u3bx8TE3P06FHm0XfffVd7HTU/P587eM8998yaNYtnNZtooOMs+o013KNGjZo0aVJcXFybNm169uy5ePHi6upqSqmT0DQYdCctd+R4rUeJiYkBAQHt2rXr2LHj448//sMPP2jQTZFJTnotbBUdHR0XF8dKXPXuhoAJCauVPnXqFCEkLy+PDzB//nyLxcKzLPH5559HRESwCwiiz+CZmZkffPDB4cOHs7Ozu3fvPmDAAPa2F/WgqayTXufl5a1fvz4/P3/Xrl0PPPBAixYt2K8n7NmzhxBy+vRp7tTDDz88ceJEntVsooGOU0obcbi7du1qMpmeeuqpr776KisrKyQk5NVXX6WUOglNg0F30nJHjlNKN23atHHjxkOHDm3ZsmXw4MGRkZFXr17VoKdCk5z0mjfZt28fIYTf+3fVuxsCxgmrmHAm2FevXo2Kitq0aROzQyRgQuPYh5cdO3YICzWYdsZrkdm//PLLbbfd9tJLL1FKXXWKi4ZwQ7aBjossbGTh7ty5c4cOHfjHr+XLl4eFhXmDgDlyXBTuy5cvt2jR4u9//7uoXGvZup7k06ZN69WrF/fCVe9uCBhHqmLCmeV2fn4+IcRw8+Vz42UwGIqKiqSWhYaGvvXWW9JyTZU447XU4IceeujRRx+llLrqIoN0CLVLGui41LzGFO577rln6NCh3MdNmzYRQqpuvJy8zM7baiThZLgdOS71on///gsWLJCWa6rESa+ZzT/99FOLFi1WrFjBXXDVuxsCxpGqm7BYLDNmzGBj2Gy2iIgI0SaO69evHxG8xo0bd9999x05cqSqqkpkWUlJiY+PzwcffCAq12BW0WuRzdXV1V27dp0zZw6/zbts2TJWp7y8XF+bOOTDLeO46FAjC/cLL7xgNpv5TxSuWLGiXbt2zOW6ni0iUB7MOmO5jONCy69du9ayZcs333xTWKjNtDNeM8vT09NNJpPwzj3bxNHwdzcEzE3nRlZWlslkysjIKCwsnDZtWnBwMNsvPnnyZLuftoSXEK9duzZv3rwvv/yyuLh4x44dd9xxR+fOnWu/3NpNpjdgGGe8/vOf/7x169bvvvvu66+/fvTRR/38/AoKCtiYS5YsCQ4OZjf/xo0bp69t9IrhduR44w73jz/+2Lx58xkzZhw/fjw3N7dNmzavvfYaC7ejs6UBJ6CbmjqyXPjulnF87ty5u3btKi4u3rNnz7Bhw0JDQ8+dO+cm0xswjDNes+6HDBnyyCOPiIZyybsbAiaiqmI2NTU1MjLSaDRaLJa9e/eykaxWa2xsrHRUoYBVVFSMGDGidevWvr6+ZrM5Li5O+LCUtK2mShS9fu655xiWtm3bjh49+ptvvuH2s0cd27ZtazKZhg4devz4cX5I+4l6O964w00pzcvLGzhwoMlk6tSpE9+FyAJqF5r2Y00ptWu56N3tyPFHHnmkXbt2RqMxIiLikUcesXvXQJsQnPH622+/JYRs27ZN5IJL3t0QMBFVZEEABEAABPRBAAKmjzjBShAAARAAAREBCJgICLIgAAIgAAL6IAAB00ecYCUIgAAIgICIAARMBARZEAABEAABfRCAgOkjTrASBEAABEBARAACJgKCLAiAAAiAgD4IQMD0ESdYCQIgAAIgICIAARMBQRYE6kPA0a+a1acvD7UhhAh/8cdVVhQXF4t+FseZns1mc0pKit2aKtlpdywUapwABEzjAYJ5cgRiY2PZD5Y3bdq0TZs2w4YN+8c//sG/Z0+mZcN/QlA0w1ZUVDj6iW0ZMxwdEv4Qe5s2bR566KH//ve/jio7Xy7sVvhD71arlVKqkjBAwJwPEGrWlQAErK7EUF9DBGJjY++///4zZ86UlpZ+/fXXixcvDgwMHDVq1K+//ipvpcsFTH64uh61Wq1xcXFnzpw5ffr0l19+OXDgwCFDhtS1E2l9/vvu+/fvJ4Ts2LGD/dz7xYsXnRSwmpoaRbaicSFgIiDIupAABMyFMNGVuwkIvzGSjf3JJ58QQv72t7+x7OXLl6dOnRoaGtq8efPf/va3Bw8epJSmp6cL1x/p6emUUrs1WScffvhh//79TSZTq1at2O9li5YyrM+goCDu/6pVq2q/6M/X17dLly5vv/02L2e2jR8/3t/f//bbb3f0kwJWq3X27Nm81YYNGwICAnh2165dAwYMMBqNYWFhCQkJTFHWr1/frFmzEydOsGrTp0/v2rXrzz//zFsJE3ZFxZFtO3fuJIRs2rTpjjvu8PX13blzp81mS0pKioqK8vPz692793vvvcc6v3Tp0uOPPx4aGurn53f77bevW7eu9tel2Vj/+c9/7r33Xn9//969ewt/2fXf//53jx49jEaj2Wzm301ey1O4wD1x4sTdd99tMpm6d+++bds2lVaKQj5I64UABEwvkYKddghIBYxS2qdPn1GjRrHaw4YNGzt27IEDB06cODF37txWrVpdvHixoqJi7ty5PXv2ZOuPiooKSqndmpTS3Nxcg8HwyiuvFBYWHjx4MCkpiVJ68eLF9u3bL1y4kPUgErD333/f19d35cqVx48fX758ucFg+PTTT5k9hJD27du/++67J0+enDVrVmBgIFv9iHwTCtjFixfHjh3729/+ltUpLS0NCAh49tlnjx07lp2dHRoampiYyA49/PDDAwYM+PXXX3Nzc319fb/66itRtzzrSMDs2sYErHfv3tu2bSsqKrp48eJrr73WrVu3LVu2fPfdd+yXMnbt2kUpjY+P79u374EDB4qLi7dv3/7hhx9yAevWrVtubu7x48cfeughs9nMRPerr75q0qTJwoULjx8/np6e7u/vzz5MCAXMZrNFR0cPHTr04MGDu3fv7tevHwSMxxEJCBjOAR0TsCtgjzzySPfu3Smln3/+eYsWLYS/O3PbbbetWbOm1mHRJUSZmoMHD540aZKUkXCJIBKwu+66Ky4ujjd5+OGHR48ezbKEEPZ705TSn376iRCyefNmXpMnrFarr69vs2bNAgICCCFdunQpLi5mR//0pz917dq1pqaGZVeuXBkYGMhu+126dKl9+/bTp09v27bt4sWLeW/ShCMBs2sbE7CcnBzWT2VlZUBAgHAVNXXq1Mcee4xSOnbs2CeffFI0HBuL/8RwQUEBIeTYsWOU0scff3z48OG8/vz583v06MGyHO/WrVubNm166tQpVr5582YIGCeGBAQM54COCdgVsIkTJ7J5MC0trUmTJs0EryZNmjz//PO1DosETKamv78/uxQmwsRnWFYu3IXYsmXLjIwMXn/FihUdO3ZkWULIxo0b+aEWLVqsX7+eZ3nCarU+8cQTJ2+8vvjii9GjR3fu3Pnq1auU0t/97ndPPPEEr3nw4EFCyA8//MBKtm7dWvvTFXfddZf8ThZHAmbXNiZgpaWlbIijR48SQgRQm/n6+losFkrppk2b/P39+/TpM3/+/D179rD6bKz9+/ez7KVLlwghu3fvppTWLqdeffVVVk4pzcnJ8fX1ra6uFq7AhPQopVeuXIGAcWJIQMBwDuiYgF0B69Wr15gxYyilS5YsiYiIYDLA/z1//nytwyIBk6kZEhLiWgETblUPCgriF82EYRBeQqSUnjlzht/YkxewF1980WAwdOzYkamdsE9h2pGA2bWNCdjly5dZD3v37iWE7Nq1iyM9efLkjz/+yI6eO3cuIyNj0qRJfn5+c+fO5ZcQ8/PzWYXLly8TQnbu3AkBE0YE6foRgIDVjxtaaYKAVMDYJg4mOdu2bTMYDPzim9DixYsXR0dH8xKZmvfee6/dS4idO3cWbjoQrsCklxCZoEp3+jkpYOfOnSOE/PWvf6WUSi8hNm/enK239uzZ07Rp002bNvXq1WvKlCncO2miIQJ29erV2t+iFO5MkfZPKX3rrbeaN28uL2DSS4g9e/ZkvfEFLruEePr0aVa+ZcsWrMDsAvfOQgiYd8a9kXhtdxv9Aw88wC5D1dTUDBkypE+fPlu3bmW/1/6nP/3pwIEDlNJ//vOfzZo1y8/PP3/+fGVlpUzNnTt3NmnShG3iOHz48JIlSxi74cOHx8TElJaWsiWdUMCys7N9fX1XrVp14sQJtomDLTjqJGBsG/2ZM2cOHjz44IMP+vn5ffvtt5RStokjPj7+2LFjOTk5fBPH1atXO3Xq9Mc//pFSevjwYZPJxDcHSoPdEAGjlL744outWrXKyMgoKir6+uuv//rXv7JLpi+//HJOTs7JkyePHj36wAMPsOuKorGEK7Cvv/6ab+LIyMhwtImjR48ew4cPP3jw4GeffXbnnXdCwKQB9doSCJjXhr4xOC58kLl169bDhg1bt26d8PbP1atXZ86cGR4e7uvr26FDh0mTJrGLXZWVlQ8++GBwcDAhhF3Ec1STUvqf//ynb9++RqMxNDR0woQJDNyXX37Zu3dvk8lEyP++iYQCRimV2UZv9zKdKBjCbfotW7a0Wq18HyOl1O42+ieffLJXr158x8ry5ctDQkL4jStR/yJRYUdFwsBXh6JLiJTSmpqaFStWdO3atXazZevWrUeOHMnuaS1atKh79+7+/v4hISHjxo37/vvv5VdglFK2jd7X1zcyMnLp0qXcTr4Co5QeP358yJAhRqOxS5cuWIFxREj87ydCUAABEAABEAABPRKAgOkxarAZBEAABEAAKzCcAyAAAiAAAvokgBWYPuMGq0EABEDA6wlAwLz+FAAAEAABENAnAQiYPuMGq0EABEDA6wlAwLz+FAAAEAABENAnAQiYPuMGq0EABEDA6wlAwLz+FAAAEAABENAnAQiYPuMGq0EABEDA6wlAwLz+FAAAEAABENAnAQiYPuMGq0EABEDA6wlAwLz+FAAAEAABENAngf8BWT4GQjP9gYwAAAAASUVORK5CYII="
+    }
+   },
+   "cell_type": "markdown",
+   "id": "7070a6e6",
+   "metadata": {},
+   "source": [
+    "# Graph Interpretatation\n",
+    "\n",
+    "Key observations text_det_box_thresh:\n",
+    "Graph 1 (Character Error Rate):\n",
+    "\n",
+    "![image.png](attachment:image.png)\n",
+    "\n",
+    "Clear positive correlation: lower thresholds yield better CER\n",
+    "Optimal zone appears to be around 0.43–0.46, achieving CER values of ~0.06–0.07\n",
+    "Above 0.65, performance degrades significantly (CER > 0.18)\n",
+    "Some variance exists, but the trend is fairly consistent\n",
+    "\n",
+    "Graph 2 (Word Error Rate):\n",
+    "\n",
+    "![image-2.png](attachment:image-2.png)\n",
+    "\n",
+    "Same general trend, but with considerably more variance/scatter\n",
+    "Best WER (~0.15) also achieved at lower thresholds\n",
+    "The spread widens dramatically as threshold increases, suggesting the model becomes unstable at higher values\n",
+    "Note: Your y-axis is still labeled \"CER\" but the title says \"Word Error Rate\" — you'll want to fix that for your thesis\n",
+    "\n",
+    "A lower detection box threshold means PaddleOCR is more permissive about what it considers a valid text region. For your Spanish business documents, being more inclusive captures more text boxes, reducing missed characters/words. However, setting it too low could introduce noise (false positives), which might explain why the absolute minimum threshold isn't always the best.\n",
+    "Recommendation for your thesis: The sweet spot looks like 0.43–0.46 for detection threshold. You might want to narrow your search space around this range and tune other parameters (like unclip_ratio or recognition thresholds) to squeeze out additional gains."
+   ]
   }
  ],
  "metadata": {