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"cells": [
{
"cell_type": "markdown",
"id": "08615cee",
"metadata": {},
"source": [
"# 03 — ResNet50 Transfer Learning\n",
"\n",
"Fine-tune a pretrained ResNet50 (ImageNet) for cattle breed classification.\n",
"\n",
"### Two-Phase Training\n",
"1. **Phase 1** (10 epochs): Freeze backbone, train only the classifier head\n",
"2. **Phase 2** (20 epochs): Unfreeze `layer3` + `layer4`, fine-tune with lower LR\n",
"\n",
"### Expected Output\n",
"- Best checkpoint → `ml/artifacts/checkpoints/resnet_best.pth`\n",
"- Training curves, confusion matrix, per-class F1\n",
"- Classification report and training summary JSON"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "457f79bb",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Project root: /Users/ajitkumarsingh/Desktop/desktop/cattle-breed-classifier-webapp\n"
]
}
],
"source": [
"import sys, os\n",
"from pathlib import Path\n",
"\n",
"PROJECT_ROOT = Path(os.getcwd()).resolve()\n",
"if 'notebooks' in str(PROJECT_ROOT):\n",
" PROJECT_ROOT = PROJECT_ROOT.parent.parent\n",
"sys.path.insert(0, str(PROJECT_ROOT))\n",
"print(f'Project root: {PROJECT_ROOT}')"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "ae58ea25",
"metadata": {},
"outputs": [
{
"ename": "InterruptedError",
"evalue": "[Errno 4] Interrupted system call",
"output_type": "error",
"traceback": [
"\u001b[31m---------------------------------------------------------------------------\u001b[39m",
"\u001b[31mInterruptedError\u001b[39m Traceback (most recent call last)",
"\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[2]\u001b[39m\u001b[32m, line 4\u001b[39m\n\u001b[32m 1\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m ml.src.utils.seed \u001b[38;5;28;01mimport\u001b[39;00m set_seed\n\u001b[32m 2\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m ml.src.utils.device \u001b[38;5;28;01mimport\u001b[39;00m get_device\n\u001b[32m 3\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m ml.src.utils.io \u001b[38;5;28;01mimport\u001b[39;00m load_config\n\u001b[32m----> \u001b[39m\u001b[32m4\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m ml.src.utils.manifests \u001b[38;5;28;01mimport\u001b[39;00m create_dataloaders\n\u001b[32m 5\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m ml.src.data.transforms \u001b[38;5;28;01mimport\u001b[39;00m get_train_transforms, get_eval_transforms\n\u001b[32m 6\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m ml.src.models.resnet \u001b[38;5;28;01mimport\u001b[39;00m CattleResNet\n\u001b[32m 7\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m ml.src.training.trainer \u001b[38;5;28;01mimport\u001b[39;00m Trainer\n",
"\u001b[36mFile \u001b[39m\u001b[32m~/Desktop/desktop/cattle-breed-classifier-webapp/ml/src/utils/manifests.py:10\u001b[39m\n\u001b[32m 7\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mtyping\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m Optional\n\u001b[32m 9\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpandas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpd\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m10\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mPIL\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m Image\n\u001b[32m 11\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mtorch\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mutils\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mdata\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m Dataset, DataLoader\n\u001b[32m 12\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mtorchvision\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m transforms\n",
"\u001b[36mFile \u001b[39m\u001b[32m<frozen importlib._bootstrap>:1360\u001b[39m, in \u001b[36m_find_and_load\u001b[39m\u001b[34m(name, import_)\u001b[39m\n",
"\u001b[36mFile \u001b[39m\u001b[32m<frozen importlib._bootstrap>:1322\u001b[39m, in \u001b[36m_find_and_load_unlocked\u001b[39m\u001b[34m(name, import_)\u001b[39m\n",
"\u001b[36mFile \u001b[39m\u001b[32m<frozen importlib._bootstrap>:1262\u001b[39m, in \u001b[36m_find_spec\u001b[39m\u001b[34m(name, path, target)\u001b[39m\n",
"\u001b[36mFile \u001b[39m\u001b[32m<frozen importlib._bootstrap_external>:1532\u001b[39m, in \u001b[36mPathFinder.find_spec\u001b[39m\u001b[34m(cls, fullname, path, target)\u001b[39m\n",
"\u001b[36mFile \u001b[39m\u001b[32m<frozen importlib._bootstrap_external>:1504\u001b[39m, in \u001b[36mPathFinder._get_spec\u001b[39m\u001b[34m(cls, fullname, path, target)\u001b[39m\n",
"\u001b[36mFile \u001b[39m\u001b[32m<frozen importlib._bootstrap_external>:1484\u001b[39m, in \u001b[36mPathFinder._path_importer_cache\u001b[39m\u001b[34m(cls, path)\u001b[39m\n",
"\u001b[31mInterruptedError\u001b[39m: [Errno 4] Interrupted system call"
]
}
],
"source": [
"from ml.src.utils.seed import set_seed\n",
"from ml.src.utils.device import get_device\n",
"from ml.src.utils.io import load_config\n",
"from ml.src.utils.manifests import create_dataloaders\n",
"from ml.src.data.transforms import get_train_transforms, get_eval_transforms\n",
"from ml.src.models.resnet import CattleResNet\n",
"from ml.src.training.trainer import Trainer\n",
"\n",
"set_seed(42)\n",
"device = get_device()"
]
},
{
"cell_type": "markdown",
"id": "c4d85602",
"metadata": {},
"source": [
"## 1. Load Config & Data"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "44de86e3",
"metadata": {},
"outputs": [],
"source": [
"config = load_config('resnet', PROJECT_ROOT / 'ml' / 'configs')\n",
"config['num_classes'] = 26\n",
"\n",
"img_size = config['image']['size']\n",
"batch_size = config['training']['batch_size']\n",
"arch = config['model']['architecture']\n",
"\n",
"print(f'Backbone: {arch[\"backbone\"]}')\n",
"print(f'Pretrained: {arch[\"pretrained\"]}')\n",
"print(f'Freeze backbone: {arch[\"freeze_backbone\"]}')\n",
"print(f'Unfreeze after: {arch[\"unfreeze_after_epochs\"]} epochs')\n",
"print(f'Unfreeze layers: {arch[\"unfreeze_layers\"]}')\n",
"print(f'Epochs: {config[\"training\"][\"num_epochs\"]}')\n",
"print(f'LR: {config[\"training\"][\"learning_rate\"]} Fine-tune LR: {config[\"training\"][\"fine_tune_lr\"]}')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a2807dbd",
"metadata": {},
"outputs": [],
"source": [
"train_transforms = get_train_transforms(img_size=img_size)\n",
"eval_transforms = get_eval_transforms(img_size=img_size)\n",
"\n",
"dataloaders = create_dataloaders(\n",
" manifests_dir=PROJECT_ROOT / 'ml' / 'artifacts' / 'manifests',\n",
" data_root=PROJECT_ROOT / 'Cattle_Resized',\n",
" train_transform=train_transforms,\n",
" eval_transform=eval_transforms,\n",
" batch_size=batch_size,\n",
" num_workers=config['data'].get('num_workers', 4),\n",
")\n",
"\n",
"class_names = dataloaders['train'].dataset.classes\n",
"print(f'Classes: {len(class_names)}')\n",
"print(f'Train: {len(dataloaders[\"train\"].dataset)} | Val: {len(dataloaders[\"val\"].dataset)} | Test: {len(dataloaders[\"test\"].dataset)}')"
]
},
{
"cell_type": "markdown",
"id": "62801587",
"metadata": {},
"source": [
"## 2. Create Model"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "60fd0584",
"metadata": {},
"outputs": [],
"source": [
"model = CattleResNet.from_config(config)\n",
"\n",
"# Count trainable vs frozen params\n",
"trainable = sum(p.numel() for p in model.parameters() if p.requires_grad)\n",
"total = sum(p.numel() for p in model.parameters())\n",
"print(f'Total parameters: {total:,}')\n",
"print(f'Trainable (Phase 1): {trainable:,} ({100*trainable/total:.1f}%)')\n",
"print(f'Frozen: {total - trainable:,}')"
]
},
{
"cell_type": "markdown",
"id": "3a783655",
"metadata": {},
"source": [
"## 3. Two-Phase Training"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "077006b5",
"metadata": {},
"outputs": [],
"source": [
"trainer = Trainer(\n",
" model=model,\n",
" config=config,\n",
" dataloaders=dataloaders,\n",
" class_names=class_names,\n",
" device=device,\n",
" model_name='resnet',\n",
")\n",
"\n",
"# Two-phase training: frozen backbone → partial fine-tuning\n",
"training_summary = trainer.train_with_phase_switch(\n",
" phase1_epochs=arch['unfreeze_after_epochs'],\n",
" phase2_lr=config['training']['fine_tune_lr'],\n",
" unfreeze_layers=arch['unfreeze_layers'],\n",
")"
]
},
{
"cell_type": "markdown",
"id": "c62c99fb",
"metadata": {},
"source": [
"## 4. Evaluate on Test Set"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b3c413c6",
"metadata": {},
"outputs": [],
"source": [
"test_results = trainer.evaluate(split='test')"
]
},
{
"cell_type": "markdown",
"id": "98c3ee02",
"metadata": {},
"source": [
"## 5. Save Artifacts"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "97f96e44",
"metadata": {},
"outputs": [],
"source": [
"trainer.save_artifacts()\n",
"\n",
"print('\\n=== ResNet50 Transfer Learning Summary ===')\n",
"print(f'Best Val Loss: {training_summary[\"best_val_loss\"]:.4f}')\n",
"print(f'Best Val Acc: {training_summary[\"best_val_accuracy\"]:.4f}')\n",
"print(f'Test Accuracy: {test_results[\"metrics\"][\"accuracy\"]:.4f}')\n",
"print(f'Test Macro F1: {test_results[\"metrics\"][\"macro_f1\"]:.4f}')\n",
"print(f'Latency: {test_results[\"latency\"][\"avg_ms\"]:.2f} ms')\n",
"print(f'Model Size: {test_results[\"model_size_mb\"]:.2f} MB')"
]
},
{
"cell_type": "markdown",
"id": "f5362b63",
"metadata": {},
"source": [
"## 6. Classification Report"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2f3951b2",
"metadata": {},
"outputs": [],
"source": [
"print(test_results['metrics']['classification_report'])"
]
},
{
"cell_type": "markdown",
"id": "764286aa",
"metadata": {},
"source": [
"---\n",
"**✅ ResNet50 Transfer Learning complete.** Proceed to `04_vit_transfer_learning.ipynb`."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Cattle Classifier (Python 3.12)",
"language": "python",
"name": "cattle-classifier"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
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