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done.\u001b[K\n", + "remote: Counting objects: 100% (3/3), done.\u001b[K\n", + "remote: Compressing objects: 100% (3/3), done.\u001b[K\n", + "remote: Total 17582 (delta 0), reused 0 (delta 0), pack-reused 17579 (from 2)\u001b[K\n", + "Receiving objects: 100% (17582/17582), 16.86 MiB | 15.17 MiB/s, done.\n", + "Resolving deltas: 100% (11973/11973), done.\n", + "/content/yolov5\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.1/1.1 MB\u001b[0m \u001b[31m22.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25h" + ] + } + ], + "source": [ + "!git clone https://github.com/ultralytics/yolov5\n", + "%cd yolov5\n", + "!pip install -r requirements.txt -q\n" + ] + }, + { + "cell_type": "code", + "source": [ + "import torch\n", + "import cv2\n", + "import matplotlib.pyplot as plt\n", + "from PIL import Image\n", + "import numpy as np\n", + "import torchvision.transforms as transforms\n", + "from torchvision import models\n", + "import torch.nn as nn\n" + ], + "metadata": { + "id": "nxvyND2CEfr9" + }, + "execution_count": 2, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "yolo = torch.hub.load('ultralytics/yolov5', 'yolov5s', pretrained=True)\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "QtJ2iv99Elwx", + "outputId": "d7db2ce9-72b7-45fb-872f-81a893f303eb" + }, + "execution_count": 3, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.12/dist-packages/torch/hub.py:330: UserWarning: You are about to download and run code from an untrusted repository. In a future release, this won't be allowed. To add the repository to your trusted list, change the command to {calling_fn}(..., trust_repo=False) and a command prompt will appear asking for an explicit confirmation of trust, or load(..., trust_repo=True), which will assume that the prompt is to be answered with 'yes'. You can also use load(..., trust_repo='check') which will only prompt for confirmation if the repo is not already trusted. This will eventually be the default behaviour\n", + " warnings.warn(\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Downloading: \"https://github.com/ultralytics/yolov5/zipball/master\" to /root/.cache/torch/hub/master.zip\n", + "Creating new Ultralytics Settings v0.0.6 file ✅ \n", + "View Ultralytics Settings with 'yolo settings' or at '/root/.config/Ultralytics/settings.json'\n", + "Update Settings with 'yolo settings key=value', i.e. 'yolo settings runs_dir=path/to/dir'. For help see https://docs.ultralytics.com/quickstart/#ultralytics-settings.\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "YOLOv5 🚀 2025-10-9 Python-3.12.11 torch-2.8.0+cu126 CPU\n", + "\n", + "Downloading https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s.pt to yolov5s.pt...\n", + "100%|██████████| 14.1M/14.1M [00:00<00:00, 93.4MB/s]\n", + "\n", + "Fusing layers... \n", + "YOLOv5s summary: 213 layers, 7225885 parameters, 0 gradients, 16.4 GFLOPs\n", + "Adding AutoShape... \n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "from google.colab import files\n", + "uploaded = files.upload() # pick car_classifier.pth from your computer\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 73 + }, + "id": "1Y_1Fr33GXOm", + "outputId": "820588a2-471f-44d8-b9ed-1d9d15ff0dab" + }, + "execution_count": 4, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + " \n", + " \n", + " Upload widget is only available when the cell has been executed in the\n", + " current browser session. Please rerun this cell to enable.\n", + " \n", + " " + ] + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Saving car_classifier.pth to car_classifier.pth\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "import os, torch\n", + "from torchvision import models, transforms\n", + "import torch.nn as nn\n", + "\n", + "# Rebuild architecture\n", + "model = models.resnet18(pretrained=False)\n", + "model.fc = nn.Linear(model.fc.in_features, 196) # 196 car classes\n", + "\n", + "# Find model file dynamically\n", + "model_path = \"car_classifier.pth\"\n", + "\n", + "# fallback: look inside \"content\" folder if not found\n", + "if not os.path.exists(model_path):\n", + " alt_path = os.path.join(\"content\", \"car_classifier.pth\")\n", + " if os.path.exists(alt_path):\n", + " model_path = alt_path\n", + " else:\n", + " raise FileNotFoundError(\"❌ Could not find car_classifier.pth in either root or /content\")\n", + "\n", + "print(f\"✅ Loading model from: {model_path}\")\n", + "model.load_state_dict(torch.load(model_path, map_location=\"cpu\"))\n", + "model.eval()\n", + "\n", + "# Define transform (for cropped car images)\n", + "transform = transforms.Compose([\n", + " transforms.Resize((224, 224)),\n", + " transforms.ToTensor(),\n", + "])\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "VJc8_THiEpyu", + "outputId": "10b91dc6-22f6-4d30-e4c8-452017637cbc" + }, + "execution_count": 5, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "✅ Loading model from: car_classifier.pth\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Stanford Cars class names (from dataset metadata)\n", + "from datasets import load_dataset\n", + "ds_meta = load_dataset(\"tanganke/stanford_cars\")\n", + "class_names = ds_meta[\"train\"].features[\"label\"].names\n" + ], + "metadata": { + "id": "m7uj1CAGI1DR", + "colab": { + "base_uri": 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and g < 50 and b < 50:\n", + " return \"Black\"\n", + " elif r > 200 and g > 200 and b < 100:\n", + " return \"Yellow\"\n", + " else:\n", + " return \"Gray/Silver\"\n" + ], + "metadata": { + "id": "Sux5PDyiEs46" + }, + "execution_count": 7, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "def detect_and_classify(img_path):\n", + " results = yolo(img_path)\n", + " detections = results.xyxy[0]\n", + " # confidences = results.boxes.conf.cpu().numpy().tolist() if hasattr(results, 'boxes') else []\n", + "\n", + " img = Image.open(img_path).convert(\"RGB\")\n", + " preds = []\n", + " for *box, conf, cls in detections:\n", + " if int(cls) == 2: # YOLO COCO class 2 = car\n", + " x1, y1, x2, y2 = map(int, box)\n", + " crop = img.crop((x1, y1, x2, y2))\n", + "\n", + " # classify\n", + " tensor = transform(crop).unsqueeze(0)\n", + " with torch.no_grad():\n", + " output = model(tensor)\n", + " pred_class = output.argmax(1).item()\n", + "\n", + " # get color\n", + " color = get_color_name(crop)\n", + "\n", + " # normalize/convert confidence to float in range ~0..1 (safe handling)\n", + " try:\n", + " conf_val = float(conf)\n", + " # if the confidence somehow came as percentage (0-100), convert to 0-1\n", + " if conf_val > 1.5:\n", + " conf_val = conf_val / 100.0\n", + " except Exception:\n", + " conf_val = None\n", + "\n", + " # append a 4-tuple now: (crop, predicted_class_index, color_name, confidence_float)\n", + " preds.append((crop, pred_class, color, conf_val))\n", + "\n", + " return preds\n" + ], + "metadata": { + "id": "JGOpRtYLExTD" + }, + "execution_count": 12, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "from google.colab import files\n", + "import matplotlib.pyplot as plt\n", + "uploaded = files.upload()\n", + "\n", + "img_path = list(uploaded.keys())[0]\n", + "results = detect_and_classify(img_path)\n", + "\n", + "print(\"Cars detected:\", len(results))\n", + "for idx, (crop, pred, color, conf) in enumerate(results):\n", + " # map to human name if class_names available\n", + " if 'class_names' in globals() and class_names is not None and isinstance(pred, int) and 0 <= pred < len(class_names):\n", + " car_name = class_names[pred]\n", + " else:\n", + " car_name = f\"Class {pred}\"\n", + "\n", + " # print with confidence if available\n", + " if conf is not None:\n", + " print(f\"Car {idx+1}: {color} {car_name} ({conf*100:.1f}% confident)\")\n", + " else:\n", + " print(f\"Car {idx+1}: {color} {car_name}\")\n", + "\n", + " # show the crop with the same title\n", + " plt.subplot(1, len(results), idx+1)\n", + " plt.imshow(crop)\n", + " title = f\"{color} {car_name}\"\n", + " if conf is not None:\n", + " title += f\"\\n({conf*100:.1f}% confident)\"\n", + " plt.title(title)\n", + " plt.axis(\"off\")\n", + "\n", + "plt.show()\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 162 + }, + "id": "UD1HuD0NE0KQ", + "outputId": "20004475-e9ad-4fef-e154-be10b0a36cf4" + }, + "execution_count": 13, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + " \n", + " \n", + " Upload widget is only available when the cell has been executed in the\n", + " current browser session. Please rerun this cell to enable.\n", + " \n", + " " + ] + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Saving venza.jpg to venza (2).jpg\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/root/.cache/torch/hub/ultralytics_yolov5_master/models/common.py:906: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n", + " with amp.autocast(autocast):\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Cars detected: 1\n", + "Car 1: Gray/Silver Dodge Dakota Crew Cab 2010 (45.9% confident)\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "2dx_3gUObSs6" + }, + "execution_count": 9, + "outputs": [] + } + ] +} \ No newline at end of file