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{
"cells": [
{
"cell_type": "markdown",
"id": "6a013a36-e156-4212-8ade-5fee79e33680",
"metadata": {},
"source": [
"Install dependencies"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "acabbaee-35be-452b-8573-4d0974fa6340",
"metadata": {},
"outputs": [],
"source": [
"!pip3 install torch torchvision torchaudio\n",
"!pip3 install matplotlib\n",
"!pip3 install ultralytics roboflow"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "fb8218b5-61c9-4fe3-b5c6-1643beb39e28",
"metadata": {},
"outputs": [],
"source": [
"import torch\n",
"from ultralytics import YOLO\n",
"from pathlib import Path\n",
"import os\n",
"import json\n",
"import yaml\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib.image as mpimg"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4bccbb25",
"metadata": {},
"outputs": [],
"source": [
"\n",
"device = \"cuda:0\" if torch.cuda.is_available() else \"cpu\"\n",
"\n",
"print(f\"Using device: {device} ({'GPU' if device != 'cpu' else 'CPU'})\")\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"\n",
"CONFIG = {\n",
" 'model': 'yolo11m.pt', # Choose model size: n, s, m, l, x\n",
" 'data': 'datasets/Hardhat-or-Hat.v1-without-hat.yolov11/data.yaml', \n",
" 'epochs': 35,\n",
" 'batch': 2 if device != 'cpu' else 4, # Adjust batch \n",
" 'imgsz': 640,\n",
" 'patience': 5,\n",
" 'device': device, \n",
"}\n",
"os.environ[\"PYTORCH_CUDA_ALLOC_CONF\"] = \"expandable_segments:True\"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d349b982",
"metadata": {},
"outputs": [],
"source": [
"\n",
"save_dir = Path('runs/detect')\n",
"save_dir.mkdir(parents=True, exist_ok=True)\n",
"\n",
"this_path = os.getcwd()\n",
"\n",
"os.environ['ULTRALYTICS_CONFIG_DIR'] = this_path\n",
"\n",
"data_file = f'{this_path}/{CONFIG['data']}'\n",
"with open(data_file, 'r') as f:\n",
" data = yaml.safe_load(f)\n",
" \n",
"\n",
"data['train'] = f'{this_path}/{CONFIG['data'].rsplit('/', 1)[0]}/train/images'\n",
"data['val'] = f'{this_path}/{CONFIG['data'].rsplit('/', 1)[0]}/valid/images'\n",
"data['test'] = f'{this_path}/{CONFIG['data'].rsplit('/', 1)[0]}/test/images'\n",
"\n",
"with open(data_file, 'w') as f:\n",
" yaml.safe_dump(data, f)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4f831042",
"metadata": {},
"outputs": [],
"source": [
"\n",
"model = YOLO(CONFIG['model'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "20208cb5",
"metadata": {},
"outputs": [],
"source": [
"\n",
"results = model.train(\n",
" data=CONFIG['data'],\n",
" epochs=CONFIG['epochs'],\n",
" batch=CONFIG['batch'],\n",
" imgsz=CONFIG['imgsz'],\n",
" patience=CONFIG['patience'],\n",
" device=CONFIG['device'],\n",
" \n",
" verbose=True,\n",
" \n",
" optimizer='SGD',\n",
" lr0=0.001,\n",
" lrf=0.01,\n",
" momentum=0.9,\n",
" weight_decay=0.0005,\n",
" warmup_epochs=3,\n",
" warmup_bias_lr=0.01,\n",
" warmup_momentum=0.8,\n",
" amp=False,\n",
" \n",
" # Augmentations\n",
" augment=True,\n",
" hsv_h=0.015, # Image HSV-Hue augmentationc\n",
" hsv_s=0.7, # Image HSV-Saturation augmentation\n",
" hsv_v=0.4, # Image HSV-Value augmentation\n",
" degrees=10, # Image rotation (+/- deg)\n",
" translate=0.1, # Image translation (+/- fraction)\n",
" scale=0.3, # Image scale (+/- gain)\n",
" shear=0.0, # Image shear (+/- deg)\n",
" perspective=0.0, # Image perspective\n",
" flipud=0.1, # Image flip up-down\n",
" fliplr=0.1, # Image flip left-right\n",
" mosaic=1.0, # Image mosaic\n",
" mixup=0.0, # Image mixup\n",
" \n",
")\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "06211243",
"metadata": {},
"outputs": [],
"source": [
"\n",
"file_path = f\"{str(results.save_dir)}\" \n",
"results_csv_path = f\"{file_path}/results.csv\" "
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e67532ea",
"metadata": {},
"outputs": [],
"source": [
"\n",
"try:\n",
" result_metrics = pd.read_csv(results_csv_path)\n",
"except FileNotFoundError:\n",
" print(f\"File not found: {results_csv_path}\")\n",
" exit()\n",
"\n",
"\n",
"metrics = {\n",
" \"Train Box Loss\": \"train/box_loss\",\n",
" \"Train Class Loss\": \"train/cls_loss\",\n",
" \"Train DFL Loss\": \"train/dfl_loss\",\n",
" \"Validation Box Loss\": \"val/box_loss\",\n",
" \"Validation Class Loss\": \"val/cls_loss\",\n",
" \"Validation DFL Loss\": \"val/dfl_loss\",\n",
" \"Precision (B)\": \"metrics/precision(B)\",\n",
" \"Recall (B)\": \"metrics/recall(B)\",\n",
" \"mAP@0.5 (B)\": \"metrics/mAP50(B)\",\n",
" \"mAP@0.5:0.95 (B)\": \"metrics/mAP50-95(B)\",\n",
"}\n",
"\n",
"%matplotlib inline\n",
"\n",
"available_metrics = {name: col for name, col in metrics.items() if col in result_metrics.columns}\n",
"missing_metrics = [name for name in metrics if name not in available_metrics]\n",
"\n",
"if missing_metrics:\n",
" print(f\"Missing metrics: {', '.join(missing_metrics)}\")\n",
"else:\n",
" print(\"All expected metrics are present.\")\n",
"\n",
"for metric_name, col in available_metrics.items():\n",
" plt.figure()\n",
" plt.plot(result_metrics[\"epoch\"], result_metrics[col], label=metric_name)\n",
" plt.title(metric_name)\n",
" plt.xlabel(\"Epoch\")\n",
" plt.ylabel(metric_name)\n",
" plt.legend()\n",
" plt.grid()\n",
" plt.show()\n",
"\n",
"final_epoch = result_metrics.iloc[-1]\n",
"final_metrics = {name: final_epoch[col] for name, col in available_metrics.items()}\n",
"\n",
"print(\"\\nFinal Metrics Summary (Last Epoch):\")\n",
"for name, value in final_metrics.items():\n",
" print(f\"{name}: {value:.4f}\")\n",
"\n",
"print(\"\\nImprovement Trends:\")\n",
"for metric_name, col in available_metrics.items():\n",
" initial = result_metrics[col].iloc[0]\n",
" final = result_metrics[col].iloc[-1]\n",
" trend = \"improved\" if final < initial else \"worsened\"\n",
" print(f\"{metric_name}: {trend} (Initial: {initial:.4f}, Final: {final:.4f})\")\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "cd2fb43f",
"metadata": {},
"outputs": [],
"source": [
"\n",
"\n",
"img = mpimg.imread(f\"{file_path}/confusion_matrix_normalized.png\") \n",
"plt.imshow(img)\n",
"plt.axis('off') \n",
"plt.show()\n",
"\n",
"img = mpimg.imread(f\"{file_path}/F1_curve.png\") \n",
"plt.imshow(img)\n",
"plt.axis('off') \n",
"plt.show()\n",
"\n",
"img = mpimg.imread(f\"{file_path}/P_curve.png\") \n",
"plt.imshow(img)\n",
"plt.axis('off') \n",
"plt.show()\n",
"\n",
"img = mpimg.imread(f\"{file_path}/R_curve.png\") \n",
"plt.imshow(img)\n",
"plt.axis('off') \n",
"plt.show()\n",
"\n",
"img = mpimg.imread(f\"{file_path}/PR_curve.png\") \n",
"plt.imshow(img)\n",
"plt.axis('off') \n",
"plt.show()\n",
"\n",
"img = mpimg.imread(f\"{file_path}/results.png\") \n",
"plt.imshow(img)\n",
"plt.axis('off') \n",
"plt.show()\n",
"\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.7"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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