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"stdout", "text": [ "Cloning into 'yolov5'...\n", "remote: Enumerating objects: 17582, 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", 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"5246969571a74f9f88cd1537751f0b7a" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "data/train-00000-of-00002.parquet: 0%| | 0.00/504M [00:00 150 and g < 100 and b < 100:\n", " return \"Red\"\n", " elif b > 150 and r < 100:\n", " return \"Blue\"\n", " elif g > 150 and r < 100:\n", " return \"Green\"\n", " elif r > 200 and g > 200 and b > 200:\n", " return \"White\"\n", " elif r < 50 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": [] } ] }