{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.12.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":3173719,"sourceType":"datasetVersion","datasetId":952827}],"dockerImageVersionId":31239,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames[:1]:\n        print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true,"execution":{"iopub.status.busy":"2026-01-14T20:24:52.738904Z","iopub.execute_input":"2026-01-14T20:24:52.739082Z","iopub.status.idle":"2026-01-14T20:25:00.900845Z","shell.execute_reply.started":"2026-01-14T20:24:52.739063Z","shell.execute_reply":"2026-01-14T20:25:00.900205Z"},"jupyter":{"source_hidden":true}},"outputs":[{"name":"stdout","text":"/kaggle/input/fruit-and-vegetable-image-recognition/validation/capsicum/Image_4.jpg\n/kaggle/input/fruit-and-vegetable-image-recognition/validation/sweetcorn/Image_4.jpg\n/kaggle/input/fruit-and-vegetable-image-recognition/validation/orange/Image_1.png\n/kaggle/input/fruit-and-vegetable-image-recognition/validation/tomato/Image_4.jpg\n/kaggle/input/fruit-and-vegetable-image-recognition/validation/turnip/Image_4.jpg\n/kaggle/input/fruit-and-vegetable-image-recognition/validation/ginger/Image_4.jpg\n/kaggle/input/fruit-and-vegetable-image-recognition/validation/raddish/Image_4.jpg\n/kaggle/input/fruit-and-vegetable-image-recognition/validation/pomegranate/Image_4.jpg\n/kaggle/input/fruit-and-vegetable-image-recognition/validation/pineapple/Image_4.jpg\n/kaggle/input/fruit-and-vegetable-image-recognition/validation/jalepeno/Image_4.jpg\n/kaggle/input/fruit-and-vegetable-image-recognition/validation/apple/Image_4.jpg\n/kaggle/input/fruit-and-vegetable-image-recognition/validation/carrot/Image_2.jpg\n/kaggle/input/fruit-and-vegetable-image-recognition/validation/lettuce/Image_4.jpg\n/kaggle/input/fruit-and-vegetable-image-recognition/validation/bell pepper/Image_4.jpg\n/kaggle/input/fruit-and-vegetable-image-recognition/validation/eggplant/Image_4.jpg\n/kaggle/input/fruit-and-vegetable-image-recognition/validation/beetroot/Image_4.jpg\n/kaggle/input/fruit-and-vegetable-image-recognition/validation/kiwi/Image_4.jpg\n/kaggle/input/fruit-and-vegetable-image-recognition/validation/pear/Image_4.jpg\n/kaggle/input/fruit-and-vegetable-image-recognition/validation/cabbage/Image_4.jpg\n/kaggle/input/fruit-and-vegetable-image-recognition/validation/cauliflower/Image_4.jpg\n/kaggle/input/fruit-and-vegetable-image-recognition/validation/paprika/Image_4.jpg\n/kaggle/input/fruit-and-vegetable-image-recognition/validation/lemon/Image_1.png\n/kaggle/input/fruit-and-vegetable-image-recognition/validation/sweetpotato/Image_4.jpg\n/kaggle/input/fruit-and-vegetable-image-recognition/validation/grapes/Image_4.jpg\n/kaggle/input/fruit-and-vegetable-image-recognition/validation/cucumber/Image_4.jpg\n/kaggle/input/fruit-and-vegetable-image-recognition/validation/corn/Image_4.jpg\n/kaggle/input/fruit-and-vegetable-image-recognition/validation/banana/Image_4.jpg\n/kaggle/input/fruit-and-vegetable-image-recognition/validation/garlic/Image_4.jpg\n/kaggle/input/fruit-and-vegetable-image-recognition/validation/chilli pepper/Image_4.jpg\n/kaggle/input/fruit-and-vegetable-image-recognition/validation/watermelon/Image_4.jpg\n/kaggle/input/fruit-and-vegetable-image-recognition/validation/mango/Image_4.jpg\n/kaggle/input/fruit-and-vegetable-image-recognition/validation/peas/Image_6.png\n/kaggle/input/fruit-and-vegetable-image-recognition/validation/onion/Image_4.jpg\n/kaggle/input/fruit-and-vegetable-image-recognition/validation/potato/Image_4.jpg\n/kaggle/input/fruit-and-vegetable-image-recognition/validation/spinach/Image_4.jpg\n/kaggle/input/fruit-and-vegetable-image-recognition/validation/soy beans/Image_4.jpg\n/kaggle/input/fruit-and-vegetable-image-recognition/test/capsicum/Image_4.jpg\n/kaggle/input/fruit-and-vegetable-image-recognition/test/sweetcorn/Image_4.jpg\n/kaggle/input/fruit-and-vegetable-image-recognition/test/orange/Image_1.png\n/kaggle/input/fruit-and-vegetable-image-recognition/test/tomato/Image_4.jpg\n/kaggle/input/fruit-and-vegetable-image-recognition/test/turnip/Image_4.jpg\n/kaggle/input/fruit-and-vegetable-image-recognition/test/ginger/Image_4.jpg\n/kaggle/input/fruit-and-vegetable-image-recognition/test/raddish/Image_4.jpg\n/kaggle/input/fruit-and-vegetable-image-recognition/test/pomegranate/Image_4.jpg\n/kaggle/input/fruit-and-vegetable-image-recognition/test/pineapple/Image_4.jpg\n/kaggle/input/fruit-and-vegetable-image-recognition/test/jalepeno/Image_4.jpg\n/kaggle/input/fruit-and-vegetable-image-recognition/test/apple/Image_4.jpg\n/kaggle/input/fruit-and-vegetable-image-recognition/test/carrot/Image_2.jpg\n/kaggle/input/fruit-and-vegetable-image-recognition/test/lettuce/Image_4.jpg\n/kaggle/input/fruit-and-vegetable-image-recognition/test/bell pepper/Image_4.jpg\n/kaggle/input/fruit-and-vegetable-image-recognition/test/eggplant/Image_4.jpg\n/kaggle/input/fruit-and-vegetable-image-recognition/test/beetroot/Image_4.jpg\n/kaggle/input/fruit-and-vegetable-image-recognition/test/kiwi/Image_4.jpg\n/kaggle/input/fruit-and-vegetable-image-recognition/test/pear/Image_4.jpg\n/kaggle/input/fruit-and-vegetable-image-recognition/test/cabbage/Image_4.jpg\n/kaggle/input/fruit-and-vegetable-image-recognition/test/cauliflower/Image_4.jpg\n/kaggle/input/fruit-and-vegetable-image-recognition/test/paprika/Image_4.jpg\n/kaggle/input/fruit-and-vegetable-image-recognition/test/lemon/Image_1.png\n/kaggle/input/fruit-and-vegetable-image-recognition/test/sweetpotato/Image_4.jpg\n/kaggle/input/fruit-and-vegetable-image-recognition/test/grapes/Image_4.jpg\n/kaggle/input/fruit-and-vegetable-image-recognition/test/cucumber/Image_4.jpg\n/kaggle/input/fruit-and-vegetable-image-recognition/test/corn/Image_4.jpg\n/kaggle/input/fruit-and-vegetable-image-recognition/test/banana/Image_4.jpg\n/kaggle/input/fruit-and-vegetable-image-recognition/test/garlic/Image_4.jpg\n/kaggle/input/fruit-and-vegetable-image-recognition/test/chilli pepper/Image_4.jpg\n/kaggle/input/fruit-and-vegetable-image-recognition/test/watermelon/Image_4.jpg\n/kaggle/input/fruit-and-vegetable-image-recognition/test/mango/Image_4.jpg\n/kaggle/input/fruit-and-vegetable-image-recognition/test/peas/Image_6.png\n/kaggle/input/fruit-and-vegetable-image-recognition/test/onion/Image_4.jpg\n/kaggle/input/fruit-and-vegetable-image-recognition/test/potato/Image_4.jpg\n/kaggle/input/fruit-and-vegetable-image-recognition/test/spinach/Image_4.jpg\n/kaggle/input/fruit-and-vegetable-image-recognition/test/soy beans/Image_4.jpg\n/kaggle/input/fruit-and-vegetable-image-recognition/train/capsicum/Image_22.jpg\n/kaggle/input/fruit-and-vegetable-image-recognition/train/sweetcorn/Image_22.jpg\n/kaggle/input/fruit-and-vegetable-image-recognition/train/orange/Image_1.png\n/kaggle/input/fruit-and-vegetable-image-recognition/train/tomato/Image_22.jpg\n/kaggle/input/fruit-and-vegetable-image-recognition/train/turnip/Image_53.jpg\n/kaggle/input/fruit-and-vegetable-image-recognition/train/ginger/Image_22.jpg\n/kaggle/input/fruit-and-vegetable-image-recognition/train/raddish/Image_22.jpg\n/kaggle/input/fruit-and-vegetable-image-recognition/train/pomegranate/Image_22.jpg\n/kaggle/input/fruit-and-vegetable-image-recognition/train/pineapple/Image_22.jpg\n/kaggle/input/fruit-and-vegetable-image-recognition/train/jalepeno/Image_22.jpg\n/kaggle/input/fruit-and-vegetable-image-recognition/train/apple/Image_69.jpg\n/kaggle/input/fruit-and-vegetable-image-recognition/train/carrot/Image_53.jpg\n/kaggle/input/fruit-and-vegetable-image-recognition/train/lettuce/Image_22.jpg\n/kaggle/input/fruit-and-vegetable-image-recognition/train/bell pepper/Image_22.jpg\n/kaggle/input/fruit-and-vegetable-image-recognition/train/eggplant/Image_22.jpg\n/kaggle/input/fruit-and-vegetable-image-recognition/train/beetroot/Image_22.jpg\n/kaggle/input/fruit-and-vegetable-image-recognition/train/kiwi/Image_22.jpg\n/kaggle/input/fruit-and-vegetable-image-recognition/train/pear/Image_22.jpg\n/kaggle/input/fruit-and-vegetable-image-recognition/train/cabbage/Image_22.jpg\n/kaggle/input/fruit-and-vegetable-image-recognition/train/cauliflower/Image_22.jpg\n/kaggle/input/fruit-and-vegetable-image-recognition/train/paprika/Image_68.jpg\n/kaggle/input/fruit-and-vegetable-image-recognition/train/lemon/Image_1.png\n/kaggle/input/fruit-and-vegetable-image-recognition/train/sweetpotato/Image_22.jpg\n/kaggle/input/fruit-and-vegetable-image-recognition/train/grapes/Image_22.jpg\n/kaggle/input/fruit-and-vegetable-image-recognition/train/cucumber/Image_22.jpg\n/kaggle/input/fruit-and-vegetable-image-recognition/train/corn/Image_22.jpg\n/kaggle/input/fruit-and-vegetable-image-recognition/train/banana/Image_69.jpg\n/kaggle/input/fruit-and-vegetable-image-recognition/train/garlic/Image_22.jpg\n/kaggle/input/fruit-and-vegetable-image-recognition/train/chilli pepper/Image_22.jpg\n/kaggle/input/fruit-and-vegetable-image-recognition/train/watermelon/Image_22.jpg\n/kaggle/input/fruit-and-vegetable-image-recognition/train/mango/Image_22.jpg\n/kaggle/input/fruit-and-vegetable-image-recognition/train/peas/Image_22.jpg\n/kaggle/input/fruit-and-vegetable-image-recognition/train/onion/Image_22.jpg\n/kaggle/input/fruit-and-vegetable-image-recognition/train/potato/Image_53.jpg\n/kaggle/input/fruit-and-vegetable-image-recognition/train/spinach/Image_22.jpg\n/kaggle/input/fruit-and-vegetable-image-recognition/train/soy beans/Image_22.jpg\n","output_type":"stream"}],"execution_count":1},{"cell_type":"code","source":"from __future__ import print_function\n\nimport argparse\nimport json\nimport os\nimport random\nimport time\n\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nfrom torchvision import datasets, transforms\nfrom torchvision.models import mobilenet_v3_small, MobileNet_V3_Small_Weights\nfrom torch.optim.lr_scheduler import StepLR\n\n\n# ────────────────────────────────────────────────────────────────\n# Utils\n# ────────────────────────────────────────────────────────────────\ndef set_seed(seed: int):\n    random.seed(seed)\n    np.random.seed(seed)\n    torch.manual_seed(seed)\n    torch.cuda.manual_seed_all(seed)\n\n\ndef count_params(model: nn.Module) -> int:\n    return sum(p.numel() for p in model.parameters() if p.requires_grad)\n\n\n@torch.no_grad()\ndef accuracy_top1(logits, targets) -> float:\n    preds = logits.argmax(dim=1)\n    return (preds == targets).float().mean().item()\n\n\ndef ensure_dir(path: str):\n    if path and not os.path.exists(path):\n        os.makedirs(path, exist_ok=True)\n\n\n# ────────────────────────────────────────────────────────────────\n# Train / Eval\n# ────────────────────────────────────────────────────────────────\ndef train_one_epoch(args, model, device, train_loader, optimizer, epoch):\n    model.train()\n    running_loss = 0.0\n    running_acc = 0.0\n    seen = 0\n\n    for batch_idx, (data, target) in enumerate(train_loader):\n        data = data.to(device, non_blocking=True)\n        target = target.to(device, non_blocking=True)\n\n        optimizer.zero_grad(set_to_none=True)\n        output = model(data)\n        loss = nn.functional.cross_entropy(output, target)\n        loss.backward()\n        optimizer.step()\n\n        bs = data.size(0)\n        seen += bs\n        running_loss += loss.item() * bs\n        running_acc += accuracy_top1(output, target) * bs\n\n        if batch_idx % args.log_interval == 0:\n            pct = 100.0 * batch_idx / len(train_loader)\n            print(\n                \"Train Epoch: {} [{}/{} ({:.0f}%)]\\tLoss: {:.6f}\\tAcc: {:.2f}%\".format(\n                    epoch,\n                    batch_idx * bs,\n                    len(train_loader.dataset),\n                    pct,\n                    loss.item(),\n                    100.0 * accuracy_top1(output, target),\n                )\n            )\n            if args.dry_run:\n                break\n\n    epoch_loss = running_loss / max(1, seen)\n    epoch_acc = 100.0 * (running_acc / max(1, seen))\n    return epoch_loss, epoch_acc\n\n\n@torch.no_grad()\ndef evaluate(model, device, loader, split_name=\"val\"):\n    model.eval()\n    loss_sum = 0.0\n    correct = 0\n    total = 0\n\n    for data, target in loader:\n        data = data.to(device, non_blocking=True)\n        target = target.to(device, non_blocking=True)\n\n        output = model(data)\n        loss = nn.functional.cross_entropy(output, target, reduction=\"sum\")\n        loss_sum += loss.item()\n\n        pred = output.argmax(dim=1)\n        correct += pred.eq(target).sum().item()\n        total += target.size(0)\n\n    avg_loss = loss_sum / max(1, total)\n    acc = 100.0 * correct / max(1, total)\n\n    print(\n        \"\\n{} set: Average loss: {:.4f}, Accuracy: {}/{} ({:.2f}%)\\n\".format(\n            split_name.capitalize(), avg_loss, correct, total, acc\n        )\n    )\n    return avg_loss, acc\n\n\n# ────────────────────────────────────────────────────────────────\n# Model\n# ────────────────────────────────────────────────────────────────\ndef build_model(num_classes: int):\n    weights = MobileNet_V3_Small_Weights.DEFAULT\n    model = mobilenet_v3_small(weights=weights)\n    in_f = model.classifier[-1].in_features\n    model.classifier[-1] = nn.Linear(in_f, num_classes)\n    return model, weights\n\n\n# ────────────────────────────────────────────────────────────────\n# Main\n# ────────────────────────────────────────────────────────────────\ndef main():\n    parser = argparse.ArgumentParser(\n        description=\"Fruit/Veg Baseline (MobileNetV3-Small) — No Dendrites\"\n    )\n\n    # ✅ Your Kaggle dataset structure (already split)\n    parser.add_argument(\n        \"--train-dir\",\n        type=str,\n        default=\"/kaggle/input/fruit-and-vegetable-image-recognition/train\",\n    )\n    parser.add_argument(\n        \"--val-dir\",\n        type=str,\n        default=\"/kaggle/input/fruit-and-vegetable-image-recognition/validation\",\n    )\n    parser.add_argument(\n        \"--test-dir\",\n        type=str,\n        default=\"/kaggle/input/fruit-and-vegetable-image-recognition/test\",\n    )\n\n    # Training hyperparams\n    parser.add_argument(\"--batch-size\", type=int, default=64, metavar=\"N\")\n    parser.add_argument(\"--test-batch-size\", type=int, default=128, metavar=\"N\")\n    parser.add_argument(\"--epochs\", type=int, default=8, metavar=\"N\")\n    parser.add_argument(\"--lr\", type=float, default=3e-4, metavar=\"LR\")\n    parser.add_argument(\"--gamma\", type=float, default=0.9, metavar=\"M\")\n    parser.add_argument(\"--weight-decay\", type=float, default=1e-4, metavar=\"WD\")\n\n    # Runtime\n    parser.add_argument(\"--no-cuda\", action=\"store_true\", default=False)\n    parser.add_argument(\"--dry-run\", action=\"store_true\", default=False)\n    parser.add_argument(\"--seed\", type=int, default=42, metavar=\"S\")\n    parser.add_argument(\"--log-interval\", type=int, default=20, metavar=\"N\")\n    parser.add_argument(\"--num-workers\", type=int, default=2)\n\n    # Outputs\n    parser.add_argument(\"--save-model\", action=\"store_true\", default=True)\n    parser.add_argument(\"--out-dir\", type=str, default=\"./outputs\")\n    parser.add_argument(\"--model-name\", type=str, default=\"fruitveg_mnv3s_baseline.pt\")\n    parser.add_argument(\"--metrics-name\", type=str, default=\"baseline_metrics.json\")\n\n    args = parser.parse_args()\n\n    # Validate dataset dirs\n    for p in [args.train_dir, args.val_dir, args.test_dir]:\n        if not os.path.isdir(p):\n            raise FileNotFoundError(f\"Directory not found: {p}\")\n\n    use_cuda = (not args.no_cuda) and torch.cuda.is_available()\n    device = torch.device(\"cuda\" if use_cuda else \"cpu\")\n    print(\"[INFO] device:\", device)\n\n    set_seed(args.seed)\n    ensure_dir(args.out_dir)\n\n    # Build once to get weights object (for eval transforms)\n    _, weights = build_model(num_classes=2)\n\n    # ImageNet normalization (explicit + stable)\n    IMAGENET_MEAN = (0.485, 0.456, 0.406)\n    IMAGENET_STD = (0.229, 0.224, 0.225)\n\n    # Train: light camera-style augmentation\n    train_transform = transforms.Compose(\n        [\n            transforms.RandomResizedCrop(224, scale=(0.8, 1.0)),\n            transforms.RandomHorizontalFlip(p=0.5),\n            transforms.ColorJitter(\n                brightness=0.15, contrast=0.15, saturation=0.10\n            ),\n            transforms.ToTensor(),\n            transforms.Normalize(IMAGENET_MEAN, IMAGENET_STD),\n        ]\n    )\n\n    # Val/Test: EXACT pretrained eval pipeline\n    eval_transform = weights.transforms()\n\n    train_ds = datasets.ImageFolder(args.train_dir, transform=train_transform)\n    val_ds = datasets.ImageFolder(args.val_dir, transform=eval_transform)\n    test_ds = datasets.ImageFolder(args.test_dir, transform=eval_transform)\n\n    # Ensure class mapping consistency\n    if val_ds.class_to_idx != train_ds.class_to_idx:\n        raise ValueError(\n            \"Class mapping mismatch train vs validation.\\n\"\n            f\"train: {train_ds.class_to_idx}\\nval: {val_ds.class_to_idx}\"\n        )\n    if test_ds.class_to_idx != train_ds.class_to_idx:\n        raise ValueError(\n            \"Class mapping mismatch train vs test.\\n\"\n            f\"train: {train_ds.class_to_idx}\\ntest: {test_ds.class_to_idx}\"\n        )\n\n    num_classes = len(train_ds.classes)\n    print(\"[INFO] num_classes:\", num_classes)\n    print(\"[INFO] classes:\", train_ds.classes)\n\n    train_loader = torch.utils.data.DataLoader(\n        train_ds,\n        batch_size=args.batch_size,\n        shuffle=True,\n        num_workers=args.num_workers,\n        pin_memory=use_cuda,\n    )\n    val_loader = torch.utils.data.DataLoader(\n        val_ds,\n        batch_size=args.test_batch_size,\n        shuffle=False,\n        num_workers=args.num_workers,\n        pin_memory=use_cuda,\n    )\n    test_loader = torch.utils.data.DataLoader(\n        test_ds,\n        batch_size=args.test_batch_size,\n        shuffle=False,\n        num_workers=args.num-workers if False else args.num_workers,  # keep consistent\n        pin_memory=use_cuda,\n    )\n\n    # Build model\n    model, _ = build_model(num_classes=num_classes)\n    model = model.to(device)\n\n    params = count_params(model)\n    print(f\"[INFO] trainable params: {params:,} ({params/1e6:.2f}M)\")\n\n    optimizer = optim.AdamW(\n        model.parameters(), lr=args.lr, weight_decay=args.weight_decay\n    )\n    scheduler = StepLR(optimizer, step_size=1, gamma=args.gamma)\n\n    best_val_acc = -1.0\n    best_state = None\n    history = []\n\n    start_time = time.time()\n\n    for epoch in range(1, args.epochs + 1):\n        tr_loss, tr_acc = train_one_epoch(\n            args, model, device, train_loader, optimizer, epoch\n        )\n        val_loss, val_acc = evaluate(\n            model, device, val_loader, split_name=\"validation\"\n        )\n\n        scheduler.step()\n\n        history.append(\n            {\n                \"epoch\": epoch,\n                \"train_loss\": tr_loss,\n                \"train_acc\": tr_acc,\n                \"val_loss\": val_loss,\n                \"val_acc\": val_acc,\n                \"lr\": optimizer.param_groups[0][\"lr\"],\n            }\n        )\n\n        # Save best\n        if val_acc > best_val_acc:\n            best_val_acc = val_acc\n            best_state = {\n                \"model_state\": model.state_dict(),\n                \"classes\": train_ds.classes,\n                \"class_to_idx\": train_ds.class_to_idx,\n                \"best_val_acc\": best_val_acc,\n                \"params\": params,\n                \"args\": vars(args),\n            }\n            if args.save_model:\n                out_path = os.path.join(args.out_dir, args.model_name)\n                torch.save(best_state, out_path)\n                print(f\"[INFO] Saved best model to {out_path}\")\n\n        if args.dry_run:\n            break\n\n    # Evaluate best checkpoint on test set\n    if best_state is not None and args.save_model:\n        ckpt_path = os.path.join(args.out_dir, args.model_name)\n        ckpt = torch.load(ckpt_path, map_location=device)\n        model.load_state_dict(ckpt[\"model_state\"])\n\n    test_loss, test_acc = evaluate(model, device, test_loader, split_name=\"test\")\n\n    elapsed = time.time() - start_time\n\n    metrics = {\n        \"best_val_acc\": best_val_acc,\n        \"final_test_acc\": test_acc,\n        \"final_test_loss\": test_loss,\n        \"params\": params,\n        \"params_million\": params / 1e6,\n        \"elapsed_seconds\": elapsed,\n        \"history\": history,\n    }\n\n    metrics_path = os.path.join(args.out_dir, args.metrics_name)\n    with open(metrics_path, \"w\") as f:\n        json.dump(metrics, f, indent=2)\n\n    print(f\"[DONE] Metrics saved to: {metrics_path}\")\n    print(f\"[DONE] Best Val Acc: {best_val_acc:.2f}% | Test Acc: {test_acc:.2f}%\")\n    print(f\"[DONE] Time: {elapsed/60:.1f} minutes\")\n\n\nif __name__ == \"__main__\":\n    import sys\n\n    sys.argv = [\"\"]  # 👈 wipes notebook-injected args\n    main()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-01-14T20:32:27.169670Z","iopub.execute_input":"2026-01-14T20:32:27.170175Z","iopub.status.idle":"2026-01-14T20:43:04.350408Z","shell.execute_reply.started":"2026-01-14T20:32:27.170147Z","shell.execute_reply":"2026-01-14T20:43:04.349606Z"}},"outputs":[{"name":"stdout","text":"[INFO] device: cuda\n[INFO] num_classes: 36\n[INFO] classes: ['apple', 'banana', 'beetroot', 'bell pepper', 'cabbage', 'capsicum', 'carrot', 'cauliflower', 'chilli pepper', 'corn', 'cucumber', 'eggplant', 'garlic', 'ginger', 'grapes', 'jalepeno', 'kiwi', 'lemon', 'lettuce', 'mango', 'onion', 'orange', 'paprika', 'pear', 'peas', 'pineapple', 'pomegranate', 'potato', 'raddish', 'soy beans', 'spinach', 'sweetcorn', 'sweetpotato', 'tomato', 'turnip', 'watermelon']\n[INFO] trainable params: 1,554,756 (1.55M)\nTrain Epoch: 1 [0/3115 (0%)]\tLoss: 3.693619\tAcc: 1.56%\n","output_type":"stream"},{"name":"stderr","text":"/usr/local/lib/python3.12/dist-packages/PIL/Image.py:1047: UserWarning: Palette images with Transparency expressed in bytes should be converted to RGBA images\n  warnings.warn(\n/usr/local/lib/python3.12/dist-packages/PIL/Image.py:1047: UserWarning: Palette images with Transparency expressed in bytes should be converted to RGBA images\n  warnings.warn(\n","output_type":"stream"},{"name":"stdout","text":"Train Epoch: 1 [1280/3115 (41%)]\tLoss: 2.610356\tAcc: 45.31%\nTrain Epoch: 1 [2560/3115 (82%)]\tLoss: 1.402538\tAcc: 64.06%\n\nValidation set: Average loss: 0.8386, Accuracy: 264/351 (75.21%)\n\n[INFO] Saved best model to ./outputs/fruitveg_mnv3s_baseline.pt\nTrain Epoch: 2 [0/3115 (0%)]\tLoss: 0.885908\tAcc: 79.69%\n","output_type":"stream"},{"name":"stderr","text":"/usr/local/lib/python3.12/dist-packages/PIL/Image.py:1047: UserWarning: Palette images with Transparency expressed in bytes should be converted to RGBA images\n  warnings.warn(\n/usr/local/lib/python3.12/dist-packages/PIL/Image.py:1047: UserWarning: Palette images with Transparency expressed in bytes should be converted to RGBA images\n  warnings.warn(\n","output_type":"stream"},{"name":"stdout","text":"Train Epoch: 2 [1280/3115 (41%)]\tLoss: 0.818186\tAcc: 78.12%\nTrain Epoch: 2 [2560/3115 (82%)]\tLoss: 0.670363\tAcc: 85.94%\n\nValidation set: Average loss: 0.4060, Accuracy: 304/351 (86.61%)\n\n[INFO] Saved best model to ./outputs/fruitveg_mnv3s_baseline.pt\nTrain Epoch: 3 [0/3115 (0%)]\tLoss: 0.429063\tAcc: 87.50%\n","output_type":"stream"},{"name":"stderr","text":"/usr/local/lib/python3.12/dist-packages/PIL/Image.py:1047: UserWarning: Palette images with Transparency expressed in bytes should be converted to RGBA images\n  warnings.warn(\n/usr/local/lib/python3.12/dist-packages/PIL/Image.py:1047: UserWarning: Palette images with Transparency expressed in bytes should be converted to RGBA images\n  warnings.warn(\n","output_type":"stream"},{"name":"stdout","text":"Train Epoch: 3 [1280/3115 (41%)]\tLoss: 0.468980\tAcc: 85.94%\nTrain Epoch: 3 [2560/3115 (82%)]\tLoss: 0.399397\tAcc: 89.06%\n\nValidation set: Average loss: 0.2879, Accuracy: 314/351 (89.46%)\n\n[INFO] Saved best model to ./outputs/fruitveg_mnv3s_baseline.pt\nTrain Epoch: 4 [0/3115 (0%)]\tLoss: 0.405832\tAcc: 85.94%\n","output_type":"stream"},{"name":"stderr","text":"/usr/local/lib/python3.12/dist-packages/PIL/Image.py:1047: UserWarning: Palette images with Transparency expressed in bytes should be converted to RGBA images\n  warnings.warn(\n/usr/local/lib/python3.12/dist-packages/PIL/Image.py:1047: UserWarning: Palette images with Transparency expressed in bytes should be converted to RGBA images\n  warnings.warn(\n","output_type":"stream"},{"name":"stdout","text":"Train Epoch: 4 [1280/3115 (41%)]\tLoss: 0.416548\tAcc: 87.50%\nTrain Epoch: 4 [2560/3115 (82%)]\tLoss: 0.443368\tAcc: 85.94%\n\nValidation set: Average loss: 0.2370, Accuracy: 320/351 (91.17%)\n\n[INFO] Saved best model to ./outputs/fruitveg_mnv3s_baseline.pt\n","output_type":"stream"},{"name":"stderr","text":"/usr/local/lib/python3.12/dist-packages/PIL/Image.py:1047: UserWarning: Palette images with Transparency expressed in bytes should be converted to RGBA images\n  warnings.warn(\n","output_type":"stream"},{"name":"stdout","text":"Train Epoch: 5 [0/3115 (0%)]\tLoss: 0.300577\tAcc: 89.06%\n","output_type":"stream"},{"name":"stderr","text":"/usr/local/lib/python3.12/dist-packages/PIL/Image.py:1047: UserWarning: Palette images with Transparency expressed in bytes should be converted to RGBA images\n  warnings.warn(\n","output_type":"stream"},{"name":"stdout","text":"Train Epoch: 5 [1280/3115 (41%)]\tLoss: 0.251591\tAcc: 92.19%\nTrain Epoch: 5 [2560/3115 (82%)]\tLoss: 0.306808\tAcc: 90.62%\n\nValidation set: Average loss: 0.2088, Accuracy: 328/351 (93.45%)\n\n[INFO] Saved best model to ./outputs/fruitveg_mnv3s_baseline.pt\nTrain Epoch: 6 [0/3115 (0%)]\tLoss: 0.290356\tAcc: 92.19%\n","output_type":"stream"},{"name":"stderr","text":"/usr/local/lib/python3.12/dist-packages/PIL/Image.py:1047: UserWarning: Palette images with Transparency expressed in bytes should be converted to RGBA images\n  warnings.warn(\n/usr/local/lib/python3.12/dist-packages/PIL/Image.py:1047: UserWarning: Palette images with Transparency expressed in bytes should be converted to RGBA images\n  warnings.warn(\n","output_type":"stream"},{"name":"stdout","text":"Train Epoch: 6 [1280/3115 (41%)]\tLoss: 0.201233\tAcc: 92.19%\nTrain Epoch: 6 [2560/3115 (82%)]\tLoss: 0.203237\tAcc: 92.19%\n\nValidation set: Average loss: 0.2043, Accuracy: 331/351 (94.30%)\n\n[INFO] Saved best model to ./outputs/fruitveg_mnv3s_baseline.pt\n","output_type":"stream"},{"name":"stderr","text":"/usr/local/lib/python3.12/dist-packages/PIL/Image.py:1047: UserWarning: Palette images with Transparency expressed in bytes should be converted to RGBA images\n  warnings.warn(\n","output_type":"stream"},{"name":"stdout","text":"Train Epoch: 7 [0/3115 (0%)]\tLoss: 0.181327\tAcc: 95.31%\n","output_type":"stream"},{"name":"stderr","text":"/usr/local/lib/python3.12/dist-packages/PIL/Image.py:1047: UserWarning: Palette images with Transparency expressed in bytes should be converted to RGBA images\n  warnings.warn(\n","output_type":"stream"},{"name":"stdout","text":"Train Epoch: 7 [1280/3115 (41%)]\tLoss: 0.101401\tAcc: 100.00%\nTrain Epoch: 7 [2560/3115 (82%)]\tLoss: 0.148472\tAcc: 95.31%\n\nValidation set: Average loss: 0.1798, Accuracy: 333/351 (94.87%)\n\n[INFO] Saved best model to ./outputs/fruitveg_mnv3s_baseline.pt\nTrain Epoch: 8 [0/3115 (0%)]\tLoss: 0.073001\tAcc: 98.44%\n","output_type":"stream"},{"name":"stderr","text":"/usr/local/lib/python3.12/dist-packages/PIL/Image.py:1047: UserWarning: Palette images with Transparency expressed in bytes should be converted to RGBA images\n  warnings.warn(\n","output_type":"stream"},{"name":"stdout","text":"Train Epoch: 8 [1280/3115 (41%)]\tLoss: 0.108986\tAcc: 96.88%\n","output_type":"stream"},{"name":"stderr","text":"/usr/local/lib/python3.12/dist-packages/PIL/Image.py:1047: UserWarning: Palette images with Transparency expressed in bytes should be converted to RGBA images\n  warnings.warn(\n","output_type":"stream"},{"name":"stdout","text":"Train Epoch: 8 [2560/3115 (82%)]\tLoss: 0.180230\tAcc: 95.31%\n\nValidation set: Average loss: 0.1786, Accuracy: 334/351 (95.16%)\n\n[INFO] Saved best model to ./outputs/fruitveg_mnv3s_baseline.pt\n\nTest set: Average loss: 0.1760, Accuracy: 342/359 (95.26%)\n\n[DONE] Metrics saved to: ./outputs/baseline_metrics.json\n[DONE] Best Val Acc: 95.16% | Test Acc: 95.26%\n[DONE] Time: 10.6 minutes\n","output_type":"stream"}],"execution_count":5},{"cell_type":"code","source":"import json\n\n# Define the path\nfile_path = './outputs/baseline_metrics.json'\n\n# Load the data\nwith open(file_path, 'r') as f:\n    metrics = json.load(f)\n\n# Example: Print a specific metric\nprint(metrics)\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-01-14T20:44:56.452000Z","iopub.execute_input":"2026-01-14T20:44:56.452317Z","iopub.status.idle":"2026-01-14T20:44:56.458055Z","shell.execute_reply.started":"2026-01-14T20:44:56.452272Z","shell.execute_reply":"2026-01-14T20:44:56.457329Z"}},"outputs":[{"name":"stdout","text":"{'best_val_acc': 95.15669515669515, 'final_test_acc': 95.26462395543176, 'final_test_loss': 0.1759721615188301, 'params': 1554756, 'params_million': 1.554756, 'elapsed_seconds': 635.7768702507019, 'history': [{'epoch': 1, 'train_loss': 2.3420290356845763, 'train_acc': 44.462279310961215, 'val_loss': 0.8386318717587028, 'val_acc': 75.21367521367522, 'lr': 0.00027}, {'epoch': 2, 'train_loss': 0.8013119672312974, 'train_acc': 77.30337076738213, 'val_loss': 0.40603443700024205, 'val_acc': 86.6096866096866, 'lr': 0.000243}, {'epoch': 3, 'train_loss': 0.47641743924797636, 'train_acc': 85.81059390622195, 'val_loss': 0.2879466738795962, 'val_acc': 89.45868945868946, 'lr': 0.0002187}, {'epoch': 4, 'train_loss': 0.34535526584469106, 'train_acc': 89.5666131678592, 'val_loss': 0.2369638318010205, 'val_acc': 91.16809116809117, 'lr': 0.00019683}, {'epoch': 5, 'train_loss': 0.26690456272128305, 'train_acc': 91.81380412743141, 'val_loss': 0.2088274290079405, 'val_acc': 93.44729344729345, 'lr': 0.000177147}, {'epoch': 6, 'train_loss': 0.207357491146217, 'train_acc': 93.73996789727127, 'val_loss': 0.2042916398442369, 'val_acc': 94.3019943019943, 'lr': 0.0001594323}, {'epoch': 7, 'train_loss': 0.16184255910340703, 'train_acc': 95.248796117057, 'val_loss': 0.17979337686826702, 'val_acc': 94.87179487179488, 'lr': 0.00014348907}, {'epoch': 8, 'train_loss': 0.1313951086246948, 'train_acc': 96.24398072305499, 'val_loss': 0.17859793796158924, 'val_acc': 95.15669515669515, 'lr': 0.000129140163}]}\n","output_type":"stream"}],"execution_count":6}]}