yolov8.ipynb

{"cells":[{"cell_type":"markdown","metadata":{"papermill":{"duration":0.005387,"end_time":"2024-02-04T19:49:37.230543","exception":false,"start_time":"2024-02-04T19:49:37.225156","status":"completed"},"tags":[]},"source":["# Minimum Working Solution DL Engima 1.0"]},{"cell_type":"markdown","metadata":{"papermill":{"duration":0.004526,"end_time":"2024-02-04T19:49:37.240361","exception":false,"start_time":"2024-02-04T19:49:37.235835","status":"completed"},"tags":[]},"source":["#### This notebook was inspired from [here](https://www.kaggle.com/code/sameen53/yolov8-minimum-working-sample)"]},{"cell_type":"markdown","metadata":{},"source":["Please upload the weights file in kaggle/working/ directory and run the code."]},{"cell_type":"code","execution_count":1,"metadata":{"execution":{"iopub.execute_input":"2024-02-04T19:49:37.251647Z","iopub.status.busy":"2024-02-04T19:49:37.251302Z","iopub.status.idle":"2024-02-04T19:49:38.000797Z","shell.execute_reply":"2024-02-04T19:49:37.999800Z"},"papermill":{"duration":0.758093,"end_time":"2024-02-04T19:49:38.003427","exception":false,"start_time":"2024-02-04T19:49:37.245334","status":"completed"},"tags":[]},"outputs":[],"source":["# basic libraries ...\n","\n","import os\n","import numpy as np\n","import pandas as pd\n","from tqdm import tqdm"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-02-04T19:49:38.015168Z","iopub.status.busy":"2024-02-04T19:49:38.014708Z","iopub.status.idle":"2024-02-04T19:49:52.478931Z","shell.execute_reply":"2024-02-04T19:49:52.477792Z"},"papermill":{"duration":14.472395,"end_time":"2024-02-04T19:49:52.481320","exception":false,"start_time":"2024-02-04T19:49:38.008925","status":"completed"},"tags":[]},"outputs":[],"source":["# ultrlytics ...\n","\n","# !pip install ultralytics -q --user"]},{"cell_type":"code","execution_count":2,"metadata":{"execution":{"iopub.execute_input":"2024-02-04T19:49:52.492883Z","iopub.status.busy":"2024-02-04T19:49:52.492556Z","iopub.status.idle":"2024-02-04T19:49:56.508875Z","shell.execute_reply":"2024-02-04T19:49:56.507885Z"},"papermill":{"duration":4.024653,"end_time":"2024-02-04T19:49:56.511131","exception":false,"start_time":"2024-02-04T19:49:52.486478","status":"completed"},"tags":[]},"outputs":[],"source":["# yolo ...\n","\n","# from ultralytics import YOLO\n","from ultralytics import YOLO"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-02-04T19:50:41.202615Z","iopub.status.busy":"2024-02-04T19:50:41.201876Z","iopub.status.idle":"2024-02-04T19:50:41.206502Z","shell.execute_reply":"2024-02-04T19:50:41.205685Z"},"papermill":{"duration":0.014551,"end_time":"2024-02-04T19:50:41.208545","exception":false,"start_time":"2024-02-04T19:50:41.193994","status":"completed"},"tags":[]},"outputs":[],"source":["# model part ..."]},{"cell_type":"code","execution_count":5,"metadata":{"execution":{"iopub.execute_input":"2024-02-04T19:50:41.223222Z","iopub.status.busy":"2024-02-04T19:50:41.222892Z","iopub.status.idle":"2024-02-04T19:50:41.228801Z","shell.execute_reply":"2024-02-04T19:50:41.228101Z"},"papermill":{"duration":0.015396,"end_time":"2024-02-04T19:50:41.230731","exception":false,"start_time":"2024-02-04T19:50:41.215335","status":"completed"},"tags":[]},"outputs":[],"source":["# configuration file that tells YOLO where to find the dataset and what objects to detect ...\n","\n","file_content = \"\"\"\n","path: dhakaAI  # dataset root dir\n","train: train/images  # train images (relative to 'path')\n","val: train/images  # val images (relative to 'path')\n","\n","# Classes\n","names:\n","    0: auto_rickshaw\n","    1: bicycle\n","    2: bus\n","    3: car\n","    4: cart_vehicle\n","    5: construction_vehicle\n","    6: motorbike\n","    7: person\n","    8: priority_vehicle\n","    9: three_wheeler\n","    10: train\n","    11: truck\n","    12: wheelchair\n","\"\"\"\n","\n","with open(\"yolov8.yaml\", mode=\"w\") as f:\n","    f.write(file_content)"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-02-05T05:25:01.827175Z","iopub.status.busy":"2024-02-05T05:25:01.826772Z","iopub.status.idle":"2024-02-05T05:25:01.831337Z","shell.execute_reply":"2024-02-05T05:25:01.830538Z"},"papermill":{"duration":5.763322,"end_time":"2024-02-05T05:25:01.833435","exception":false,"start_time":"2024-02-05T05:24:56.070113","status":"completed"},"tags":[]},"outputs":[],"source":["# inference part ..."]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-02-05T05:25:12.665322Z","iopub.status.busy":"2024-02-05T05:25:12.664441Z","iopub.status.idle":"2024-02-05T05:28:26.976396Z","shell.execute_reply":"2024-02-05T05:28:26.975365Z"},"papermill":{"duration":203.405739,"end_time":"2024-02-05T05:28:30.764294","exception":false,"start_time":"2024-02-05T05:25:07.358555","status":"completed"},"tags":[]},"outputs":[],"source":["# # get prediction boxes string according to the submission format ...\n","\n","# def get_prediction_string(boxes, scores, classes):\n","#     pred_strs = []\n","#     for i, score in enumerate(scores):\n","#         single_pred_str = \"\"\n","#         single_pred_str += str(float(classes[i])) + \" \" + str(float(score)) + \" \"\n","        \n","#         x_center , y_center, width,height = boxes[i]\n","#         x1 = float(x_center) - (float(width) / 2)\n","#         y1 = float(y_center) - (float(height) / 2)\n","#         width = float(width)\n","#         height = float(height)\n","#         #single_pred_str += \" \".join(str(float(x)) for x in boxes[i])\n","#         single_pred_str +=  f\"{x1} {y1} {width} {height}\"\n","        \n","#         pred_strs.append(single_pred_str)\n","#     ans = ','.join(map(str, pred_strs))\n","#     if len(ans):\n","#         return ans\n","# #     the solution metrics faield in case of a NaN, '' (empty). So, return \"0 0 0 0 0 0\" for NaN, '' (empty) string\n","#     return \"0 0 0 0 0 0\"\n","\n","# # get the predcition in id, ImageID, PredictionString_pred foramt ...\n","\n","# def get_prediction_entry(i, filename, boxes, scores, classes):\n","#     return {\n","#         \"id\": i, # strating from 0 ...\n","#         \"ImageID\": filename.split('.')[0], # before the extension ...\n","#         \"PredictionString_pred\": get_prediction_string(boxes, scores, classes)\n","#     }\n","\n","# # Directory path ...\n","# test_directory = \"custom_dataset\\images\\\\test\"\n","\n","# # Load the model ...\n","# # model = YOLO('/kaggle/working/runs/detect/train/weights/best.pt')\n","# model = YOLO(\"yolov8\\\\results\\\\runs\\detect\\\\train\\weights\\\\best.pt\")\n","\n","\n","# # do the inference ...\n","\n","# def predict_all_files(test_directory):\n","#     predictions = []\n","#     for i,filename in tqdm(enumerate(os.listdir(test_directory))):\n","#         if filename.endswith(\".jpg\"):\n","#             filepath = os.path.join(test_directory, filename)\n","#             results = model.predict(source=filepath, conf=0.50, verbose=False)\n","#             boxes = results[0].boxes.xywhn\n","#             scores = results[0].boxes.conf\n","#             classes = results[0].boxes.cls\n","#             prediction = get_prediction_entry(i, filename, boxes, scores, classes)\n","#             predictions.append(prediction)\n","# #             to csv format ...\n","#     predictions_df = pd.DataFrame(predictions)\n","#     predictions_df.to_csv(\"submission.csv\", index=False)\n","\n","# # call the inference function ...\n","# predict_all_files(test_directory)"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-02-05T05:28:42.025243Z","iopub.status.busy":"2024-02-05T05:28:42.024855Z","iopub.status.idle":"2024-02-05T05:28:42.052014Z","shell.execute_reply":"2024-02-05T05:28:42.051231Z"},"papermill":{"duration":5.828172,"end_time":"2024-02-05T05:28:42.054299","exception":false,"start_time":"2024-02-05T05:28:36.226127","status":"completed"},"tags":[]},"outputs":[],"source":["# load the submission dataframe ....\n","\n","# submission_df = pd.read_csv('submission.csv')"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-02-05T05:28:52.960999Z","iopub.status.busy":"2024-02-05T05:28:52.960602Z","iopub.status.idle":"2024-02-05T05:28:52.973912Z","shell.execute_reply":"2024-02-05T05:28:52.973070Z"},"papermill":{"duration":5.565407,"end_time":"2024-02-05T05:28:52.975947","exception":false,"start_time":"2024-02-05T05:28:47.410540","status":"completed"},"tags":[]},"outputs":[],"source":["# preview ...\n","\n","# submission_df.tail()"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-02-05T05:29:04.468439Z","iopub.status.busy":"2024-02-05T05:29:04.468023Z","iopub.status.idle":"2024-02-05T05:29:04.473926Z","shell.execute_reply":"2024-02-05T05:29:04.473073Z"},"papermill":{"duration":5.716935,"end_time":"2024-02-05T05:29:04.475946","exception":false,"start_time":"2024-02-05T05:28:58.759011","status":"completed"},"tags":[]},"outputs":[],"source":["# dataframe shape ...\n","\n","# submission_df.shape"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-02-05T05:29:15.827574Z","iopub.status.busy":"2024-02-05T05:29:15.827201Z","iopub.status.idle":"2024-02-05T05:29:15.835633Z","shell.execute_reply":"2024-02-05T05:29:15.834767Z"},"papermill":{"duration":5.923038,"end_time":"2024-02-05T05:29:15.837459","exception":false,"start_time":"2024-02-05T05:29:09.914421","status":"completed"},"tags":[]},"outputs":[],"source":["# any null value? ...\n","\n","# submission_df.isnull().sum()"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-02-05T05:29:26.759103Z","iopub.status.busy":"2024-02-05T05:29:26.758724Z","iopub.status.idle":"2024-02-05T05:29:26.831927Z","shell.execute_reply":"2024-02-05T05:29:26.830914Z"},"papermill":{"duration":5.63123,"end_time":"2024-02-05T05:29:26.834375","exception":false,"start_time":"2024-02-05T05:29:21.203145","status":"completed"},"tags":[]},"outputs":[],"source":["# acceptable submission format ...\n","\n","# submission_df.to_csv('submission.csv',index=False)"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-02-05T05:29:38.271305Z","iopub.status.busy":"2024-02-05T05:29:38.270932Z","iopub.status.idle":"2024-02-05T05:29:38.276782Z","shell.execute_reply":"2024-02-05T05:29:38.275846Z"},"papermill":{"duration":5.591344,"end_time":"2024-02-05T05:29:38.278727","exception":false,"start_time":"2024-02-05T05:29:32.687383","status":"completed"},"tags":[]},"outputs":[],"source":["# # make a dataframe with the actual labels\n","# test_directory = \"custom_dataset\\labels\\\\test\"\n","# actual_labels = []\n","\n","# for i,filename in tqdm(enumerate(os.listdir(test_directory))):\n","#     if filename.endswith(\".txt\"):\n","#         labels = []\n","#         filepath = os.path.join(test_directory, filename)\n","#         filename = filename.split('.')[0]\n","#         with open(filepath, \"r\") as f:\n","#             lines = f.readlines()\n","#             for line in lines:\n","#                 line = line.strip()\n","#                 labels.append(line)\n","#             # labels = str(labels)\n","#             actual_labels.append({\n","#                 \"ImageID\": filename,\n","#                 \"Label\": labels\n","#             })\n","\n","# actual_labels_df = pd.DataFrame(actual_labels)\n","\n","# # preview ...\n","# actual_labels_df.iloc[0].Label"]},{"cell_type":"code","execution_count":6,"id":"912413cb","metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Ultralytics YOLOv8.1.18 🚀 Python-3.10.1 torch-2.1.2+cpu CPU (Intel Core(TM) i5-8250U 1.60GHz)\n","Model summary (fused): 268 layers, 43616631 parameters, 0 gradients, 164.9 GFLOPs\n"]},{"name":"stderr","output_type":"stream","text":["\u001b[34m\u001b[1mval: \u001b[0mScanning F:\\Academics\\4-2\\CSE 472\\Project\\DLEnigma\\datasets\\dhakaAI\\train\\labels.cache... 2804 images, 0 backgrounds, 0 corrupt: 100%|██████████| 2804/2804 [00:00<?, ?it/s]"]},{"name":"stdout","output_type":"stream","text":["\u001b[34m\u001b[1mval: \u001b[0mWARNING ⚠️ F:\\Academics\\4-2\\CSE 472\\Project\\DLEnigma\\datasets\\dhakaAI\\train\\images\\144.jpg: corrupt JPEG restored and saved\n","\u001b[34m\u001b[1mval: \u001b[0mWARNING ⚠️ F:\\Academics\\4-2\\CSE 472\\Project\\DLEnigma\\datasets\\dhakaAI\\train\\images\\145.jpg: corrupt JPEG restored and saved\n","\u001b[34m\u001b[1mval: \u001b[0mWARNING ⚠️ F:\\Academics\\4-2\\CSE 472\\Project\\DLEnigma\\datasets\\dhakaAI\\train\\images\\146.jpg: corrupt JPEG restored and saved\n","\u001b[34m\u001b[1mval: \u001b[0mWARNING ⚠️ F:\\Academics\\4-2\\CSE 472\\Project\\DLEnigma\\datasets\\dhakaAI\\train\\images\\147.jpg: corrupt JPEG restored and saved\n","\u001b[34m\u001b[1mval: \u001b[0mWARNING ⚠️ F:\\Academics\\4-2\\CSE 472\\Project\\DLEnigma\\datasets\\dhakaAI\\train\\images\\148.jpg: corrupt JPEG restored and saved\n","\u001b[34m\u001b[1mval: \u001b[0mWARNING ⚠️ F:\\Academics\\4-2\\CSE 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472\\Project\\DLEnigma\\datasets\\dhakaAI\\train\\images\\Dipto_352.jpg: corrupt JPEG restored and saved\n","\u001b[34m\u001b[1mval: \u001b[0mWARNING ⚠️ F:\\Academics\\4-2\\CSE 472\\Project\\DLEnigma\\datasets\\dhakaAI\\train\\images\\Dipto_353.jpg: corrupt JPEG restored and saved\n","\u001b[34m\u001b[1mval: \u001b[0mWARNING ⚠️ F:\\Academics\\4-2\\CSE 472\\Project\\DLEnigma\\datasets\\dhakaAI\\train\\images\\Dipto_360.jpg: corrupt JPEG restored and saved\n","\u001b[34m\u001b[1mval: \u001b[0mWARNING ⚠️ F:\\Academics\\4-2\\CSE 472\\Project\\DLEnigma\\datasets\\dhakaAI\\train\\images\\Dipto_363.jpg: corrupt JPEG restored and saved\n","\u001b[34m\u001b[1mval: \u001b[0mWARNING ⚠️ F:\\Academics\\4-2\\CSE 472\\Project\\DLEnigma\\datasets\\dhakaAI\\train\\images\\Dipto_364.jpg: corrupt JPEG restored and saved\n","\u001b[34m\u001b[1mval: \u001b[0mWARNING ⚠️ F:\\Academics\\4-2\\CSE 472\\Project\\DLEnigma\\datasets\\dhakaAI\\train\\images\\Dipto_366.jpg: corrupt JPEG restored and saved\n"]},{"name":"stderr","output_type":"stream","text":["\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 351/351 [1:58:26<00:00, 20.25s/it]\n"]},{"name":"stdout","output_type":"stream","text":["                   all       2804      13044      0.628      0.478      0.468      0.251\n","               bicycle       2804        459      0.622      0.316      0.356       0.12\n","                   bus       2804       3333      0.777      0.392      0.504      0.342\n","                   car       2804       5476      0.517      0.537      0.406      0.246\n","             motorbike       2804       2284      0.698      0.539      0.561      0.207\n","                 truck       2804       1492      0.526      0.608      0.514      0.342\n","Speed: 7.0ms preprocess, 2399.5ms inference, 0.0ms loss, 2.7ms postprocess per image\n","Results saved to \u001b[1mruns\\detect\\val2\u001b[0m\n"]}],"source":["model = YOLO(\"runs\\\\detect\\\\train\\\\weights\\\\best.pt\")\n","results = model.val(data='yolov8.yaml', batch=8)"]},{"cell_type":"code","execution_count":7,"id":"a374f3a3","metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["ultralytics.utils.metrics.DetMetrics object with attributes:\n","\n","ap_class_index: array([ 1,  2,  3,  6, 11])\n","box: ultralytics.utils.metrics.Metric object\n","confusion_matrix: <ultralytics.utils.metrics.ConfusionMatrix object at 0x000002E11EB6B490>\n","curves: ['Precision-Recall(B)', 'F1-Confidence(B)', 'Precision-Confidence(B)', 'Recall-Confidence(B)']\n","curves_results: [[array([          0,    0.001001,    0.002002,    0.003003,    0.004004,    0.005005,    0.006006,    0.007007,    0.008008,    0.009009,     0.01001,    0.011011,    0.012012,    0.013013,    0.014014,    0.015015,    0.016016,    0.017017,    0.018018,    0.019019,     0.02002,    0.021021,    0.022022,    0.023023,\n","          0.024024,    0.025025,    0.026026,    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  0.25138,     0.25138,     0.25138,     0.34248,     0.25138])\n","names: {0: 'auto_rickshaw', 1: 'bicycle', 2: 'bus', 3: 'car', 4: 'cart_vehicle', 5: 'construction_vehicle', 6: 'motorbike', 7: 'person', 8: 'priority_vehicle', 9: 'three_wheeler', 10: 'train', 11: 'truck', 12: 'wheelchair'}\n","plot: True\n","results_dict: {'metrics/precision(B)': 0.6278537768765812, 'metrics/recall(B)': 0.47822690137846713, 'metrics/mAP50(B)': 0.4681389867429198, 'metrics/mAP50-95(B)': 0.2513782910482446, 'fitness': 0.27305436061771216}\n","save_dir: WindowsPath('runs/detect/val2')\n","speed: {'preprocess': 7.024359856114407, 'inference': 2399.540264579267, 'loss': 0.005570951099912722, 'postprocess': 2.7479388234278614}\n","task: 'detect'\n"]}],"source":["print(results)"]},{"cell_type":"code","execution_count":8,"id":"b9bbc0f8","metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["{'metrics/precision(B)': 0.6278537768765812, 'metrics/recall(B)': 0.47822690137846713, 'metrics/mAP50(B)': 0.4681389867429198, 'metrics/mAP50-95(B)': 0.2513782910482446, 'fitness': 0.27305436061771216}\n"]}],"source":["# save the results ...\n","print(results.results_dict)"]},{"cell_type":"code","execution_count":9,"id":"c6de1f0e","metadata":{},"outputs":[],"source":["# save the result dicts in a text file\n","with open(\"results_yolov8.txt\", \"w\") as f:\n","    f.write(str(results.results_dict))"]}],"metadata":{"kaggle":{"accelerator":"gpu","dataSources":[{"databundleVersionId":7527339,"sourceId":67836,"sourceType":"competition"}],"dockerImageVersionId":30636,"isGpuEnabled":true,"isInternetEnabled":true,"language":"python","sourceType":"notebook"},"kernelspec":{"display_name":"Python 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