{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.7.6","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"gpu","dataSources":[{"sourceType":"competition","sourceId":24800,"datasetId":1042002,"databundleVersionId":1831594},{"sourceType":"datasetVersion","sourceId":1799839,"datasetId":1069682,"databundleVersionId":1837296},{"sourceType":"datasetVersion","sourceId":1800066,"datasetId":1069809,"databundleVersionId":1837523},{"sourceType":"datasetVersion","sourceId":1800067,"datasetId":1069810,"databundleVersionId":1837524},{"sourceType":"datasetVersion","sourceId":1800825,"datasetId":1070245,"databundleVersionId":1838284},{"sourceType":"datasetVersion","sourceId":1800777,"datasetId":1069787,"databundleVersionId":1838236},{"sourceType":"datasetVersion","sourceId":1800778,"datasetId":1070222,"databundleVersionId":1838237}],"dockerImageVersionId":30034,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"!pip install --upgrade seaborn","metadata":{"_kg_hide-input":true,"_kg_hide-output":true,"execution":{"iopub.status.busy":"2025-12-01T09:18:36.253585Z","iopub.execute_input":"2025-12-01T09:18:36.253894Z","iopub.status.idle":"2025-12-01T09:18:43.439517Z","shell.execute_reply.started":"2025-12-01T09:18:36.25387Z","shell.execute_reply":"2025-12-01T09:18:43.438792Z"},"papermill":{"duration":9.633907,"end_time":"2021-01-01T09:44:53.448657","exception":false,"start_time":"2021-01-01T09:44:43.81475","status":"completed"},"tags":[],"trusted":true,"collapsed":true,"jupyter":{"outputs_hidden":true}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"import numpy as np, pandas as pd\nfrom glob import glob\nimport shutil, os\nimport matplotlib.pyplot as plt\nfrom sklearn.model_selection import GroupKFold\nfrom tqdm.notebook import tqdm\nimport seaborn as sns","metadata":{"execution":{"iopub.status.busy":"2025-12-01T09:19:17.700641Z","iopub.execute_input":"2025-12-01T09:19:17.700946Z","iopub.status.idle":"2025-12-01T09:19:18.425459Z","shell.execute_reply.started":"2025-12-01T09:19:17.700921Z","shell.execute_reply":"2025-12-01T09:19:18.424796Z"},"papermill":{"duration":0.926929,"end_time":"2021-01-01T09:44:54.403588","exception":false,"start_time":"2021-01-01T09:44:53.476659","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"dim = 512 #512, 256, 'original'\nfold = 4","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-12-01T09:19:21.694179Z","iopub.execute_input":"2025-12-01T09:19:21.694495Z","iopub.status.idle":"2025-12-01T09:19:21.698212Z","shell.execute_reply.started":"2025-12-01T09:19:21.69447Z","shell.execute_reply":"2025-12-01T09:19:21.697479Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"train_df = pd.read_csv(f'../input/vinbigdata-{dim}-image-dataset/vinbigdata/train.csv')\ntrain_df.head()","metadata":{"execution":{"iopub.status.busy":"2025-12-01T09:19:23.971153Z","iopub.execute_input":"2025-12-01T09:19:23.97153Z","iopub.status.idle":"2025-12-01T09:19:24.107617Z","shell.execute_reply.started":"2025-12-01T09:19:23.971497Z","shell.execute_reply":"2025-12-01T09:19:24.106774Z"},"papermill":{"duration":0.262045,"end_time":"2021-01-01T09:44:54.691965","exception":false,"start_time":"2021-01-01T09:44:54.42992","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"train_df['image_path'] = f'/kaggle/input/vinbigdata-{dim}-image-dataset/vinbigdata/train/'+train_df.image_id+('.png' if dim!='original' else '.jpg')\ntrain_df.head()","metadata":{"execution":{"iopub.status.busy":"2025-12-01T09:19:26.383067Z","iopub.execute_input":"2025-12-01T09:19:26.383379Z","iopub.status.idle":"2025-12-01T09:19:26.435861Z","shell.execute_reply.started":"2025-12-01T09:19:26.383351Z","shell.execute_reply":"2025-12-01T09:19:26.43513Z"},"papermill":{"duration":0.086788,"end_time":"2021-01-01T09:44:54.805857","exception":false,"start_time":"2021-01-01T09:44:54.719069","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# 14 Class","metadata":{"papermill":{"duration":0.027478,"end_time":"2021-01-01T09:44:54.861374","exception":false,"start_time":"2021-01-01T09:44:54.833896","status":"completed"},"tags":[]}},{"cell_type":"code","source":"train_df = train_df[train_df.class_id!=14].reset_index(drop = True)","metadata":{"execution":{"iopub.status.busy":"2025-12-01T09:19:30.804608Z","iopub.execute_input":"2025-12-01T09:19:30.804902Z","iopub.status.idle":"2025-12-01T09:19:30.831043Z","shell.execute_reply.started":"2025-12-01T09:19:30.804877Z","shell.execute_reply":"2025-12-01T09:19:30.83035Z"},"papermill":{"duration":0.05543,"end_time":"2021-01-01T09:44:54.944088","exception":false,"start_time":"2021-01-01T09:44:54.888658","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# Pre-Processing","metadata":{"papermill":{"duration":0.027303,"end_time":"2021-01-01T09:44:54.999199","exception":false,"start_time":"2021-01-01T09:44:54.971896","status":"completed"},"tags":[]}},{"cell_type":"code","source":"train_df['x_min'] = train_df.apply(lambda row: (row.x_min)/row.width, axis =1)\ntrain_df['y_min'] = train_df.apply(lambda row: (row.y_min)/row.height, axis =1)\n\ntrain_df['x_max'] = train_df.apply(lambda row: (row.x_max)/row.width, axis =1)\ntrain_df['y_max'] = train_df.apply(lambda row: (row.y_max)/row.height, axis =1)\n\ntrain_df['x_mid'] = train_df.apply(lambda row: (row.x_max+row.x_min)/2, axis =1)\ntrain_df['y_mid'] = train_df.apply(lambda row: (row.y_max+row.y_min)/2, axis =1)\n\ntrain_df['w'] = train_df.apply(lambda row: (row.x_max-row.x_min), axis =1)\ntrain_df['h'] = train_df.apply(lambda row: (row.y_max-row.y_min), axis =1)\n\ntrain_df['area'] = train_df['w']*train_df['h']\ntrain_df.head()","metadata":{"execution":{"iopub.status.busy":"2025-12-01T09:19:32.677868Z","iopub.execute_input":"2025-12-01T09:19:32.67815Z","iopub.status.idle":"2025-12-01T09:19:37.397943Z","shell.execute_reply.started":"2025-12-01T09:19:32.678125Z","shell.execute_reply":"2025-12-01T09:19:37.397064Z"},"papermill":{"duration":7.821668,"end_time":"2021-01-01T09:45:02.854149","exception":false,"start_time":"2021-01-01T09:44:55.032481","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"features = ['x_min', 'y_min', 'x_max', 'y_max', 'x_mid', 'y_mid', 'w', 'h', 'area']\nX = train_df[features]\ny = train_df['class_id']\nX.shape, y.shape","metadata":{"execution":{"iopub.status.busy":"2025-12-01T09:19:39.826061Z","iopub.execute_input":"2025-12-01T09:19:39.826361Z","iopub.status.idle":"2025-12-01T09:19:39.835691Z","shell.execute_reply.started":"2025-12-01T09:19:39.826333Z","shell.execute_reply":"2025-12-01T09:19:39.834856Z"},"papermill":{"duration":0.040387,"end_time":"2021-01-01T09:45:02.923416","exception":false,"start_time":"2021-01-01T09:45:02.883029","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"class_ids, class_names = list(zip(*set(zip(train_df.class_id, train_df.class_name))))\nclasses = list(np.array(class_names)[np.argsort(class_ids)])\nclasses = list(map(lambda x: str(x), classes))\nclasses","metadata":{"execution":{"iopub.status.busy":"2025-12-01T09:19:42.183035Z","iopub.execute_input":"2025-12-01T09:19:42.18334Z","iopub.status.idle":"2025-12-01T09:19:42.197917Z","shell.execute_reply.started":"2025-12-01T09:19:42.183315Z","shell.execute_reply":"2025-12-01T09:19:42.197104Z"},"papermill":{"duration":0.050418,"end_time":"2021-01-01T09:45:03.002944","exception":false,"start_time":"2021-01-01T09:45:02.952526","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# t-SNE Visualization","metadata":{"papermill":{"duration":0.028555,"end_time":"2021-01-01T09:45:03.060819","exception":false,"start_time":"2021-01-01T09:45:03.032264","status":"completed"},"tags":[]}},{"cell_type":"code","source":"# %%time\n# from sklearn.manifold import TSNE\n\n# tsne = TSNE(n_components = 2, perplexity = 40, random_state=1, n_iter=5000)\n# data_X = X\n# data_y = y.loc[data_X.index]\n# embs = tsne.fit_transform(data_X)\n# # Add to dataframe for convenience\n# plot_x = embs[:, 0]\n# plot_y = embs[:, 1]","metadata":{"_kg_hide-output":true,"execution":{"iopub.status.busy":"2025-11-29T13:46:52.009148Z","iopub.execute_input":"2025-11-29T13:46:52.009483Z","execution_failed":"2025-11-29T14:00:57.267Z"},"papermill":{"duration":79.550362,"end_time":"2021-01-01T09:46:22.640073","exception":false,"start_time":"2021-01-01T09:45:03.089711","status":"completed"},"tags":[],"_kg_hide-input":true,"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# import matplotlib.pyplot as plt\n# plt.figure(figsize = (15, 15))\n# plt.axis('off')\n# scatter = plt.scatter(plot_x, plot_y, marker = 'o',s = 50, c=data_y.tolist(), alpha= 0.5,cmap='viridis')\n# plt.legend(handles=scatter.legend_elements()[0], labels=classes)","metadata":{"execution":{"execution_failed":"2025-11-29T14:00:57.267Z"},"papermill":{"duration":0.674101,"end_time":"2021-01-01T09:46:23.353693","exception":false,"start_time":"2021-01-01T09:46:22.679592","status":"completed"},"tags":[],"_kg_hide-input":true,"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# BBox Location","metadata":{"papermill":{"duration":0.043674,"end_time":"2021-01-01T09:46:23.441245","exception":false,"start_time":"2021-01-01T09:46:23.397571","status":"completed"},"tags":[]}},{"cell_type":"markdown","source":"## x_mid Vs y_mid","metadata":{"papermill":{"duration":0.041604,"end_time":"2021-01-01T09:46:23.525588","exception":false,"start_time":"2021-01-01T09:46:23.483984","status":"completed"},"tags":[]}},{"cell_type":"code","source":"# from scipy.stats import gaussian_kde\n\n\n# x_val = train_df.x_mid.values\n# y_val = train_df.y_mid.values\n\n# # Calculate the point density\n# xy = np.vstack([x_val,y_val])\n# z = gaussian_kde(xy)(xy)\n\n# fig, ax = plt.subplots(figsize = (10, 10))\n# ax.axis('off')\n# ax.scatter(x_val, y_val, c=z, s=100, cmap='viridis')\n# # ax.set_xlabel('x_mid')\n# # ax.set_ylabel('y_mid')\n# plt.show()","metadata":{"execution":{"execution_failed":"2025-11-29T14:00:57.267Z"},"papermill":{"duration":31.402628,"end_time":"2021-01-01T09:46:54.970613","exception":false,"start_time":"2021-01-01T09:46:23.567985","status":"completed"},"tags":[],"_kg_hide-input":true,"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"## bbox_w Vs bbox_h","metadata":{"papermill":{"duration":0.046802,"end_time":"2021-01-01T09:46:55.064644","exception":false,"start_time":"2021-01-01T09:46:55.017842","status":"completed"},"tags":[]}},{"cell_type":"code","source":"# x_val = train_df.w.values\n# y_val = train_df.h.values\n\n# # Calculate the point density\n# xy = np.vstack([x_val,y_val])\n# z = gaussian_kde(xy)(xy)\n\n# fig, ax = plt.subplots(figsize = (10, 10))\n# ax.axis('off')\n# ax.scatter(x_val, y_val, c=z, s=100, cmap='viridis')\n# # ax.set_xlabel('bbox_width')\n# # ax.set_ylabel('bbox_height')\n# plt.show()","metadata":{"execution":{"execution_failed":"2025-11-29T14:00:57.268Z"},"papermill":{"duration":30.485279,"end_time":"2021-01-01T09:47:25.596636","exception":false,"start_time":"2021-01-01T09:46:55.111357","status":"completed"},"tags":[],"_kg_hide-input":true,"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# Image Aspect Ratio","metadata":{"papermill":{"duration":0.090619,"end_time":"2021-01-01T09:47:25.783368","exception":false,"start_time":"2021-01-01T09:47:25.692749","status":"completed"},"tags":[]}},{"cell_type":"code","source":"# x_val = train_df.width.values\n# y_val = train_df.height.values\n\n# # Calculate the point density\n# xy = np.vstack([x_val,y_val])\n# z = gaussian_kde(xy)(xy)\n\n# fig, ax = plt.subplots(figsize = (10, 10))\n# ax.axis('off')\n# ax.scatter(x_val, y_val, c=z, s=100, cmap='viridis')\n# # ax.set_xlabel('image_width')\n# # ax.set_ylabel('image_height')\n# plt.show()","metadata":{"execution":{"execution_failed":"2025-11-29T14:00:57.268Z"},"papermill":{"duration":30.131145,"end_time":"2021-01-01T09:47:56.005954","exception":false,"start_time":"2021-01-01T09:47:25.874809","status":"completed"},"tags":[],"_kg_hide-input":true,"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# Split","metadata":{"papermill":{"duration":0.052492,"end_time":"2021-01-01T09:47:56.110766","exception":false,"start_time":"2021-01-01T09:47:56.058274","status":"completed"},"tags":[]}},{"cell_type":"code","source":"gkf  = GroupKFold(n_splits = 5)\ntrain_df['fold'] = -1\nfor fold, (train_idx, val_idx) in enumerate(gkf.split(train_df, groups = train_df.image_id.tolist())):\n    train_df.loc[val_idx, 'fold'] = fold\ntrain_df.head()","metadata":{"execution":{"iopub.status.busy":"2025-12-01T09:19:47.594123Z","iopub.execute_input":"2025-12-01T09:19:47.594439Z","iopub.status.idle":"2025-12-01T09:19:47.656159Z","shell.execute_reply.started":"2025-12-01T09:19:47.594412Z","shell.execute_reply":"2025-12-01T09:19:47.655461Z"},"papermill":{"duration":0.134603,"end_time":"2021-01-01T09:47:56.297774","exception":false,"start_time":"2021-01-01T09:47:56.163171","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"train_files = []\nval_files   = []\nval_files += list(train_df[train_df.fold==fold].image_path.unique())\ntrain_files += list(train_df[train_df.fold!=fold].image_path.unique())","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# Copying Files","metadata":{"papermill":{"duration":0.083752,"end_time":"2021-01-01T09:47:56.584924","exception":false,"start_time":"2021-01-01T09:47:56.501172","status":"completed"},"tags":[]}},{"cell_type":"code","source":"os.makedirs('/kaggle/working/vinbigdata/labels/train', exist_ok = True)\nos.makedirs('/kaggle/working/vinbigdata/labels/val', exist_ok = True)\nos.makedirs('/kaggle/working/vinbigdata/images/train', exist_ok = True)\nos.makedirs('/kaggle/working/vinbigdata/images/val', exist_ok = True)\nlabel_dir = '/kaggle/input/vinbigdata-yolo-labels-dataset/labels'\n\nfor file in tqdm(train_files):\n    shutil.copy(file, '/kaggle/working/vinbigdata/images/train')\n    filename = file.split('/')[-1].split('.')[0]\n    shutil.copy(os.path.join(label_dir, filename+'.txt'), '/kaggle/working/vinbigdata/labels/train')\n    \nfor file in tqdm(val_files):\n    shutil.copy(file, '/kaggle/working/vinbigdata/images/val')\n    filename = file.split('/')[-1].split('.')[0]\n    shutil.copy(os.path.join(label_dir, filename+'.txt'), '/kaggle/working/vinbigdata/labels/val')","metadata":{"execution":{"iopub.status.busy":"2025-12-01T09:20:02.035224Z","iopub.execute_input":"2025-12-01T09:20:02.0356Z","iopub.status.idle":"2025-12-01T09:20:41.52162Z","shell.execute_reply.started":"2025-12-01T09:20:02.035571Z","shell.execute_reply":"2025-12-01T09:20:41.520759Z"},"papermill":{"duration":124.654777,"end_time":"2021-01-01T09:50:01.331041","exception":false,"start_time":"2021-01-01T09:47:56.676264","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# Get Class Name","metadata":{"papermill":{"duration":0.068822,"end_time":"2021-01-01T09:50:01.458337","exception":false,"start_time":"2021-01-01T09:50:01.389515","status":"completed"},"tags":[]}},{"cell_type":"code","source":"class_ids, class_names = list(zip(*set(zip(train_df.class_id, train_df.class_name))))\nclasses = list(np.array(class_names)[np.argsort(class_ids)])\nclasses = list(map(lambda x: str(x), classes))\nclasses","metadata":{"execution":{"iopub.status.busy":"2025-12-01T09:20:47.276211Z","iopub.execute_input":"2025-12-01T09:20:47.276694Z","iopub.status.idle":"2025-12-01T09:20:47.300955Z","shell.execute_reply.started":"2025-12-01T09:20:47.276661Z","shell.execute_reply":"2025-12-01T09:20:47.299888Z"},"papermill":{"duration":0.082234,"end_time":"2021-01-01T09:50:01.601574","exception":false,"start_time":"2021-01-01T09:50:01.51934","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"from os import listdir\nfrom os.path import isfile, join\nimport yaml\n\ncwd = '/kaggle/working/'\n\nwith open(join( cwd , 'train.txt'), 'w') as f:\n    for path in glob('/kaggle/working/vinbigdata/images/train/*'):\n        f.write(path+'\\n')\n            \nwith open(join( cwd , 'val.txt'), 'w') as f:\n    for path in glob('/kaggle/working/vinbigdata/images/val/*'):\n        f.write(path+'\\n')\n\ndata = dict(\n    train =  join( cwd , 'train.txt') ,\n    val   =  join( cwd , 'val.txt' ),\n    nc    = 14,\n    names = classes\n    )\n\nwith open(join( cwd , 'vinbigdata.yaml'), 'w') as outfile:\n    yaml.dump(data, outfile, default_flow_style=False)\n\nf = open(join( cwd , 'vinbigdata.yaml'), 'r')\nprint('\\nyaml:')\nprint(f.read())","metadata":{"execution":{"iopub.status.busy":"2025-12-01T09:20:49.083246Z","iopub.execute_input":"2025-12-01T09:20:49.083604Z","iopub.status.idle":"2025-12-01T09:20:49.138943Z","shell.execute_reply.started":"2025-12-01T09:20:49.083572Z","shell.execute_reply":"2025-12-01T09:20:49.138243Z"},"papermill":{"duration":0.113001,"end_time":"2021-01-01T09:50:01.996448","exception":false,"start_time":"2021-01-01T09:50:01.883447","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# https://www.kaggle.com/ultralytics/yolov5\n# !git clone https://github.com/ultralytics/yolov5  # clone repo\n# %cd yolov5\nshutil.copytree('/kaggle/input/yolov5-official-v31-dataset/yolov5', '/kaggle/working/yolov5')\nos.chdir('/kaggle/working/yolov5')\n# %pip install -qr requirements.txt # install dependencies\n\nimport torch\nfrom IPython.display import Image, clear_output  # to display images\n\nclear_output()\nprint('Setup complete. Using torch %s %s' % (torch.__version__, torch.cuda.get_device_properties(0) if torch.cuda.is_available() else 'CPU'))","metadata":{"_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","execution":{"iopub.status.busy":"2025-12-01T09:20:51.600643Z","iopub.execute_input":"2025-12-01T09:20:51.60093Z","iopub.status.idle":"2025-12-01T09:20:53.702349Z","shell.execute_reply.started":"2025-12-01T09:20:51.600906Z","shell.execute_reply":"2025-12-01T09:20:53.701441Z"},"papermill":{"duration":6.702428,"end_time":"2021-01-01T09:50:08.784153","exception":false,"start_time":"2021-01-01T09:50:02.081725","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"!python detect.py --weights yolov5s.pt --img 512 --conf 0.25 --source data/images/\nImage(filename='runs/detect/exp/zidane.jpg', width=512)","metadata":{"execution":{"iopub.status.busy":"2025-12-01T09:22:48.307509Z","iopub.execute_input":"2025-12-01T09:22:48.307837Z","iopub.status.idle":"2025-12-01T09:22:53.048762Z","shell.execute_reply.started":"2025-12-01T09:22:48.307811Z","shell.execute_reply":"2025-12-01T09:22:53.047372Z"},"papermill":{"duration":10.410768,"end_time":"2021-01-01T09:50:19.303402","exception":false,"start_time":"2021-01-01T09:50:08.892634","status":"completed"},"tags":[],"trusted":true,"collapsed":true,"jupyter":{"outputs_hidden":true}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# Selecting Models\nusing `v5s`. To select your prefered model just replace `--cfg models/yolov5s.yaml --weights yolov5s.pt` with the following command:\n* `v5s` : `--cfg models/yolov5s.yaml --weights yolov5s.pt`\n* `v5m` : `--cfg models/yolov5m.yaml --weights yolov5m.pt`\n* `v5l` : `--cfg models/yolov5l.yaml --weights yolov5l.pt`\n* `v5x` : `--cfg models/yolov5x.yaml --weights yolov5x.pt`","metadata":{"papermill":{"duration":0.064016,"end_time":"2021-01-01T09:50:19.564859","exception":false,"start_time":"2021-01-01T09:50:19.500843","status":"completed"},"tags":[]}},{"cell_type":"markdown","source":"# Train","metadata":{"papermill":{"duration":0.064553,"end_time":"2021-01-01T09:50:19.6938","exception":false,"start_time":"2021-01-01T09:50:19.629247","status":"completed"},"tags":[]}},{"cell_type":"code","source":"# !WANDB_MODE=\"dryrun\" python train.py --img 512 --batch 16 --epochs 3 --data coco128.yaml --weights yolov5s.pt --nosave --cache \n!WANDB_MODE=\"dryrun\" python train.py --img 512 --batch 16 --epochs 30 --data /kaggle/working/vinbigdata.yaml --weights yolov5x.pt --cache","metadata":{"execution":{"iopub.status.busy":"2025-12-01T09:23:05.157571Z","iopub.execute_input":"2025-12-01T09:23:05.157925Z","iopub.status.idle":"2025-12-01T11:06:59.689254Z","shell.execute_reply.started":"2025-12-01T09:23:05.157891Z","shell.execute_reply":"2025-12-01T11:06:59.688346Z"},"papermill":{"duration":19916.498298,"end_time":"2021-01-01T15:22:16.289734","exception":false,"start_time":"2021-01-01T09:50:19.791436","status":"completed"},"tags":[],"trusted":true,"collapsed":true,"jupyter":{"outputs_hidden":true}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# Class Distribution","metadata":{"papermill":{"duration":4.919442,"end_time":"2021-01-01T15:22:26.398681","exception":false,"start_time":"2021-01-01T15:22:21.479239","status":"completed"},"tags":[]}},{"cell_type":"code","source":"plt.figure(figsize = (20,20))\nplt.axis('off')\nplt.imshow(plt.imread('runs/train/exp/labels_correlogram.jpg'));","metadata":{"execution":{"iopub.status.busy":"2025-12-01T11:23:02.353051Z","iopub.execute_input":"2025-12-01T11:23:02.353428Z","iopub.status.idle":"2025-12-01T11:23:03.156033Z","shell.execute_reply.started":"2025-12-01T11:23:02.353394Z","shell.execute_reply":"2025-12-01T11:23:03.155287Z"},"papermill":{"duration":6.511035,"end_time":"2021-01-01T15:22:37.753063","exception":false,"start_time":"2021-01-01T15:22:31.242028","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"plt.figure(figsize = (20,20))\nplt.axis('off')\nplt.imshow(plt.imread('runs/train/exp/labels.jpg'));","metadata":{"execution":{"iopub.status.busy":"2025-12-01T11:23:21.135328Z","iopub.execute_input":"2025-12-01T11:23:21.135651Z","iopub.status.idle":"2025-12-01T11:23:21.755075Z","shell.execute_reply.started":"2025-12-01T11:23:21.135626Z","shell.execute_reply":"2025-12-01T11:23:21.754018Z"},"papermill":{"duration":5.977042,"end_time":"2021-01-01T15:22:48.614609","exception":false,"start_time":"2021-01-01T15:22:42.637567","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# Batch Image","metadata":{"papermill":{"duration":5.378338,"end_time":"2021-01-01T15:22:59.482837","exception":false,"start_time":"2021-01-01T15:22:54.104499","status":"completed"},"tags":[]}},{"cell_type":"code","source":"import matplotlib.pyplot as plt\nplt.figure(figsize = (15, 15))\nplt.imshow(plt.imread('runs/train/exp/train_batch0.jpg'))\n\nplt.figure(figsize = (15, 15))\nplt.imshow(plt.imread('runs/train/exp/train_batch1.jpg'))\n\nplt.figure(figsize = (15, 15))\nplt.imshow(plt.imread('runs/train/exp/train_batch2.jpg'))","metadata":{"execution":{"iopub.status.busy":"2025-12-01T11:23:31.289797Z","iopub.execute_input":"2025-12-01T11:23:31.290119Z","iopub.status.idle":"2025-12-01T11:23:32.933475Z","shell.execute_reply.started":"2025-12-01T11:23:31.290088Z","shell.execute_reply":"2025-12-01T11:23:32.932662Z"},"papermill":{"duration":7.317416,"end_time":"2021-01-01T15:23:11.777544","exception":false,"start_time":"2021-01-01T15:23:04.460128","status":"completed"},"tags":[],"_kg_hide-input":true,"trusted":true,"collapsed":true,"jupyter":{"outputs_hidden":true}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# GT Vs Pred","metadata":{}},{"cell_type":"code","source":"fig, ax = plt.subplots(3, 2, figsize = (2*5,3*5), constrained_layout = True)\nfor row in range(3):\n    ax[row][0].imshow(plt.imread(f'runs/train/exp/test_batch{row}_labels.jpg'))\n    ax[row][0].set_xticks([])\n    ax[row][0].set_yticks([])\n    ax[row][0].set_title(f'runs/train/exp/test_batch{row}_labels.jpg', fontsize = 12)\n    \n    ax[row][1].imshow(plt.imread(f'runs/train/exp/test_batch{row}_pred.jpg'))\n    ax[row][1].set_xticks([])\n    ax[row][1].set_yticks([])\n    ax[row][1].set_title(f'runs/train/exp/test_batch{row}_pred.jpg', fontsize = 12)","metadata":{"execution":{"iopub.status.busy":"2025-12-01T11:23:40.692552Z","iopub.execute_input":"2025-12-01T11:23:40.692875Z","iopub.status.idle":"2025-12-01T11:23:42.231183Z","shell.execute_reply.started":"2025-12-01T11:23:40.692845Z","shell.execute_reply":"2025-12-01T11:23:42.230417Z"},"papermill":{"duration":6.453975,"end_time":"2021-01-01T15:23:23.514717","exception":false,"start_time":"2021-01-01T15:23:17.060742","status":"completed"},"tags":[],"_kg_hide-input":true,"trusted":true,"collapsed":true,"jupyter":{"outputs_hidden":true}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# (Loss, Map) Vs Epoch","metadata":{}},{"cell_type":"code","source":"plt.figure(figsize=(30,15))\nplt.axis('off')\nplt.imshow(plt.imread('runs/train/exp/results.png'));","metadata":{"_kg_hide-input":true,"trusted":true,"execution":{"iopub.status.busy":"2025-12-01T11:23:50.465677Z","iopub.execute_input":"2025-12-01T11:23:50.46598Z","iopub.status.idle":"2025-12-01T11:23:51.162014Z","shell.execute_reply.started":"2025-12-01T11:23:50.465955Z","shell.execute_reply":"2025-12-01T11:23:51.161285Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# Confusion Matrix","metadata":{}},{"cell_type":"code","source":"plt.figure(figsize=(30,15))\nplt.axis('off')\nplt.imshow(plt.imread('runs/train/exp/confusion_matrix.png'));","metadata":{"trusted":true,"_kg_hide-input":true,"execution":{"iopub.status.busy":"2025-12-01T11:24:44.686056Z","iopub.execute_input":"2025-12-01T11:24:44.686422Z","iopub.status.idle":"2025-12-01T11:24:45.758829Z","shell.execute_reply.started":"2025-12-01T11:24:44.686386Z","shell.execute_reply":"2025-12-01T11:24:45.758064Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# Inference","metadata":{"papermill":{"duration":4.941983,"end_time":"2021-01-01T15:23:33.765831","exception":false,"start_time":"2021-01-01T15:23:28.823848","status":"completed"},"tags":[]}},{"cell_type":"code","source":"!python detect.py --weights 'runs/train/exp/weights/best.pt'\\\n--img 512\\\n--conf 0.15\\\n--iou 0.5\\\n--source /kaggle/working/vinbigdata/images/val\\\n--exist-ok","metadata":{"_kg_hide-output":true,"execution":{"execution_failed":"2025-11-29T14:00:57.269Z"},"papermill":{"duration":10.763143,"end_time":"2021-01-01T15:23:49.800461","exception":false,"start_time":"2021-01-01T15:23:39.037318","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# Inference Plot","metadata":{"papermill":{"duration":5.225725,"end_time":"2021-01-01T15:24:00.706026","exception":false,"start_time":"2021-01-01T15:23:55.480301","status":"completed"},"tags":[]}},{"cell_type":"code","source":"import matplotlib.pyplot as plt\nfrom mpl_toolkits.axes_grid1 import ImageGrid\nimport numpy as np\nimport random\nimport cv2\nfrom glob import glob\nfrom tqdm import tqdm\n\nfiles = glob('runs/detect/exp/*')\nfor _ in range(3):\n    row = 4\n    col = 4\n    grid_files = random.sample(files, row*col)\n    images     = []\n    for image_path in tqdm(grid_files):\n        img          = cv2.cvtColor(cv2.imread(image_path), cv2.COLOR_BGR2RGB)\n        images.append(img)\n\n    fig = plt.figure(figsize=(col*5, row*5))\n    grid = ImageGrid(fig, 111,  # similar to subplot(111)\n                     nrows_ncols=(col, row),  # creates 2x2 grid of axes\n                     axes_pad=0.05,  # pad between axes in inch.\n                     )\n\n    for ax, im in zip(grid, images):\n        # Iterating over the grid returns the Axes.\n        ax.imshow(im)\n        ax.set_xticks([])\n        ax.set_yticks([])\n    plt.show()","metadata":{"execution":{"iopub.status.busy":"2025-12-01T11:25:03.228141Z","iopub.execute_input":"2025-12-01T11:25:03.22848Z","iopub.status.idle":"2025-12-01T11:25:03.293947Z","shell.execute_reply.started":"2025-12-01T11:25:03.228453Z","shell.execute_reply":"2025-12-01T11:25:03.292635Z"},"papermill":{"duration":5.31015,"end_time":"2021-01-01T15:24:11.211904","exception":false,"start_time":"2021-01-01T15:24:05.901754","status":"completed"},"tags":[],"trusted":true,"_kg_hide-input":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# shutil.rmtree('/kaggle/working/vinbigdata')\n# shutil.rmtree('runs/detect')\n# for file in (glob('runs/train/exp/**/*.png', recursive = True)+glob('runs/train/exp/**/*.jpg', recursive = True)):\n#     os.remove(file)","metadata":{"execution":{"execution_failed":"2025-11-29T14:00:57.269Z"},"papermill":{"duration":5.709202,"end_time":"2021-01-01T15:24:22.413173","exception":false,"start_time":"2021-01-01T15:24:16.703971","status":"completed"},"tags":[],"trusted":true,"_kg_hide-input":true,"_kg_hide-output":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"!ls runs/train/exp/weights/","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-12-01T11:33:43.90588Z","iopub.execute_input":"2025-12-01T11:33:43.90619Z","iopub.status.idle":"2025-12-01T11:33:44.883236Z","shell.execute_reply.started":"2025-12-01T11:33:43.906162Z","shell.execute_reply":"2025-12-01T11:33:44.882431Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"!zip -r runs_weight.zip . runs/","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-12-01T11:37:28.645018Z","iopub.execute_input":"2025-12-01T11:37:28.645375Z","iopub.status.idle":"2025-12-01T11:37:57.099105Z","shell.execute_reply.started":"2025-12-01T11:37:28.645345Z","shell.execute_reply":"2025-12-01T11:37:57.09815Z"},"collapsed":true,"jupyter":{"outputs_hidden":true}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"!cp runs_weight.zip /kaggle/working/","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-12-01T11:41:51.464475Z","iopub.execute_input":"2025-12-01T11:41:51.46483Z","iopub.status.idle":"2025-12-01T11:41:53.150688Z","shell.execute_reply.started":"2025-12-01T11:41:51.464802Z","shell.execute_reply":"2025-12-01T11:41:53.149559Z"}},"outputs":[],"execution_count":null}]}