{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"gpuType":"T4","authorship_tag":"ABX9TyMBay77HipF/d8uNRViA80Z"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"},"accelerator":"GPU"},"cells":[{"cell_type":"code","execution_count":2,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Tl3QbOOckg0q","executionInfo":{"status":"ok","timestamp":1779961008802,"user_tz":-330,"elapsed":29835,"user":{"displayName":"Krishna Verma","userId":"18291293214161594665"}},"outputId":"1eeb2466-a582-412c-e30f-163c089fd7d4"},"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}],"source":["from google.colab import drive\n","drive.mount('/content/drive')"]},{"cell_type":"code","source":["!pip install ultralytics"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"collapsed":true,"id":"KeUOEZVfknCp","executionInfo":{"status":"ok","timestamp":1779961066282,"user_tz":-330,"elapsed":9562,"user":{"displayName":"Krishna Verma","userId":"18291293214161594665"}},"outputId":"98d93c47-da56-4938-9568-83dfc5564ded"},"execution_count":6,"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting ultralytics\n","  Downloading ultralytics-8.4.56-py3-none-any.whl.metadata (39 kB)\n","Requirement already satisfied: numpy>=1.23.0 in /usr/local/lib/python3.12/dist-packages (from ultralytics) (2.0.2)\n","Requirement already satisfied: matplotlib>=3.3.0 in /usr/local/lib/python3.12/dist-packages (from ultralytics) (3.10.0)\n","Requirement already satisfied: opencv-python>=4.6.0 in /usr/local/lib/python3.12/dist-packages (from ultralytics) (4.13.0.92)\n","Requirement already satisfied: pillow>=7.1.2 in /usr/local/lib/python3.12/dist-packages (from ultralytics) 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kB)\n","Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.12/dist-packages (from matplotlib>=3.3.0->ultralytics) (1.3.3)\n","Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.12/dist-packages (from matplotlib>=3.3.0->ultralytics) (0.12.1)\n","Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.12/dist-packages (from matplotlib>=3.3.0->ultralytics) (4.63.0)\n","Requirement already satisfied: kiwisolver>=1.3.1 in /usr/local/lib/python3.12/dist-packages (from matplotlib>=3.3.0->ultralytics) (1.5.0)\n","Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.12/dist-packages (from matplotlib>=3.3.0->ultralytics) (26.2)\n","Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.12/dist-packages (from matplotlib>=3.3.0->ultralytics) (3.3.2)\n","Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.12/dist-packages (from matplotlib>=3.3.0->ultralytics) 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(4.15.0)\n","Requirement already satisfied: setuptools<82 in /usr/local/lib/python3.12/dist-packages (from torch>=1.8.0->ultralytics) (75.2.0)\n","Requirement already satisfied: sympy>=1.13.3 in /usr/local/lib/python3.12/dist-packages (from torch>=1.8.0->ultralytics) (1.14.0)\n","Requirement already satisfied: networkx>=2.5.1 in /usr/local/lib/python3.12/dist-packages (from torch>=1.8.0->ultralytics) (3.6.1)\n","Requirement already satisfied: jinja2 in /usr/local/lib/python3.12/dist-packages (from torch>=1.8.0->ultralytics) (3.1.6)\n","Requirement already satisfied: fsspec>=0.8.5 in /usr/local/lib/python3.12/dist-packages (from torch>=1.8.0->ultralytics) (2025.3.0)\n","Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.12/dist-packages (from python-dateutil>=2.7->matplotlib>=3.3.0->ultralytics) (1.17.0)\n","Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.12/dist-packages (from sympy>=1.13.3->torch>=1.8.0->ultralytics) (1.3.0)\n","Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.12/dist-packages (from jinja2->torch>=1.8.0->ultralytics) (3.0.3)\n","Downloading ultralytics-8.4.56-py3-none-any.whl (1.3 MB)\n","\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m43.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading ultralytics_thop-2.0.19-py3-none-any.whl (28 kB)\n","Installing collected packages: ultralytics-thop, ultralytics\n","Successfully installed ultralytics-8.4.56 ultralytics-thop-2.0.19\n"]}]},{"cell_type":"code","source":["!pip install roboflow"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"collapsed":true,"id":"UkIxkmsFmIwS","executionInfo":{"status":"ok","timestamp":1779961056718,"user_tz":-330,"elapsed":15507,"user":{"displayName":"Krishna Verma","userId":"18291293214161594665"}},"outputId":"35999534-9da8-46fa-c904-cbe497ffcf3b"},"execution_count":5,"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting roboflow\n","  Downloading roboflow-1.3.8-py3-none-any.whl.metadata (11 kB)\n","Requirement already satisfied: certifi in /usr/local/lib/python3.12/dist-packages (from roboflow) (2026.5.20)\n","Collecting idna==3.7 (from roboflow)\n","  Downloading idna-3.7-py3-none-any.whl.metadata (9.9 kB)\n","Requirement already satisfied: cycler in /usr/local/lib/python3.12/dist-packages (from roboflow) (0.12.1)\n","Requirement already satisfied: kiwisolver>=1.3.1 in /usr/local/lib/python3.12/dist-packages (from roboflow) (1.5.0)\n","Requirement already satisfied: matplotlib in /usr/local/lib/python3.12/dist-packages (from roboflow) (3.10.0)\n","Requirement already satisfied: numpy>=1.18.5 in /usr/local/lib/python3.12/dist-packages (from roboflow) (2.0.2)\n","Collecting opencv-python-headless==4.10.0.84 (from roboflow)\n","  Downloading opencv_python_headless-4.10.0.84-cp37-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (20 kB)\n","Requirement already satisfied: Pillow>=7.1.2 in /usr/local/lib/python3.12/dist-packages (from roboflow) (11.3.0)\n","Collecting pi-heif<2 (from roboflow)\n","  Downloading pi_heif-1.3.0-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl.metadata (6.3 kB)\n","Collecting pillow-avif-plugin<2 (from roboflow)\n","  Downloading pillow_avif_plugin-1.5.5-cp312-cp312-manylinux_2_28_x86_64.whl.metadata (2.2 kB)\n","Requirement already satisfied: python-dateutil in /usr/local/lib/python3.12/dist-packages (from roboflow) (2.9.0.post0)\n","Requirement already satisfied: python-dotenv in /usr/local/lib/python3.12/dist-packages (from roboflow) (1.2.2)\n","Requirement already satisfied: requests in /usr/local/lib/python3.12/dist-packages (from roboflow) (2.32.4)\n","Requirement already satisfied: six in /usr/local/lib/python3.12/dist-packages (from 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opencv-python-headless 4.13.0.92\n","    Uninstalling opencv-python-headless-4.13.0.92:\n","      Successfully uninstalled opencv-python-headless-4.13.0.92\n","  Attempting uninstall: idna\n","    Found existing installation: idna 3.15\n","    Uninstalling idna-3.15:\n","      Successfully uninstalled idna-3.15\n","Successfully installed filetype-1.2.0 idna-3.7 opencv-python-headless-4.10.0.84 pi-heif-1.3.0 pillow-avif-plugin-1.5.5 roboflow-1.3.8\n"]}]},{"cell_type":"code","source":["\n","\n","from roboflow import Roboflow\n","rf = Roboflow(api_key=\"0gwtO7p9PJa39qtM9vUX\")\n","project = rf.workspace(\"project-5\").project(\"traffic-signal-eym6e\")\n","version = project.version(1)\n","dataset = version.download(\"yolov8\")\n",""],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"collapsed":true,"id":"7lOafonzmqXG","executionInfo":{"status":"ok","timestamp":1779961134487,"user_tz":-330,"elapsed":46732,"user":{"displayName":"Krishna Verma","userId":"18291293214161594665"}},"outputId":"2190759d-6d4e-4e8f-b346-73312f13063f"},"execution_count":8,"outputs":[{"output_type":"stream","name":"stdout","text":["loading Roboflow workspace...\n","loading Roboflow project...\n"]},{"output_type":"stream","name":"stderr","text":["Downloading Dataset Version Zip in traffic-signal-1 to yolov8:: 100%|██████████| 577486/577486 [00:35<00:00, 16385.22it/s]"]},{"output_type":"stream","name":"stdout","text":["\n"]},{"output_type":"stream","name":"stderr","text":["\n","Extracting Dataset Version Zip to traffic-signal-1 in yolov8:: 100%|██████████| 29697/29697 [00:06<00:00, 4672.01it/s]\n"]}]},{"cell_type":"code","source":["from ultralytics import YOLO\n","import shutil, os"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"UL1KvzNemkz5","executionInfo":{"status":"ok","timestamp":1779961087754,"user_tz":-330,"elapsed":11209,"user":{"displayName":"Krishna Verma","userId":"18291293214161594665"}},"outputId":"f680d497-1254-4d7f-cff1-62399a6e9314"},"execution_count":7,"outputs":[{"output_type":"stream","name":"stdout","text":["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"]}]},{"cell_type":"code","source":["model = YOLO(\"yolov8n.pt\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"HWV55dnWnzL_","executionInfo":{"status":"ok","timestamp":1779961190462,"user_tz":-330,"elapsed":246,"user":{"displayName":"Krishna Verma","userId":"18291293214161594665"}},"outputId":"d30226b5-2ec4-4a84-ce66-b64d1e1251d2"},"execution_count":10,"outputs":[{"output_type":"stream","name":"stdout","text":["\r\u001b[KDownloading https://github.com/ultralytics/assets/releases/download/v8.4.0/yolov8n.pt to 'yolov8n.pt': 100% ━━━━━━━━━━━━ 6.2MB 243.2MB/s 0.0s\n"]}]},{"cell_type":"code","source":["results = model.train(\n","    data=dataset.location + \"/data.yaml\",\n","    epochs=25,\n","    imgsz=640,\n","    batch=16,\n","    workers=2,\n","    device=0,\n",")\n","\n","print(\"Model Trained\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":499},"id":"ZUgn_y_RnH7D","executionInfo":{"status":"error","timestamp":1779961195901,"user_tz":-330,"elapsed":107,"user":{"displayName":"Krishna Verma","userId":"18291293214161594665"}},"outputId":"8215d958-e843-4b22-95ad-653860d489d8"},"execution_count":11,"outputs":[{"output_type":"stream","name":"stdout","text":["Ultralytics 8.4.56 🚀 Python-3.12.13 torch-2.11.0+cpu \n"]},{"output_type":"error","ename":"ValueError","evalue":"Invalid CUDA 'device=0' requested. Use 'device=cpu' or pass valid CUDA device(s) if available, i.e. 'device=0' or 'device=0,1,2,3' for Multi-GPU.\n\ntorch.cuda.is_available(): False\ntorch.cuda.device_count(): 0\nos.environ['CUDA_VISIBLE_DEVICES']: None\nSee https://pytorch.org/get-started/locally/ for up-to-date torch install instructions if no CUDA devices are seen by torch.\n","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)","\u001b[0;32m/tmp/ipykernel_3908/1761746133.py\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m results = model.train(\n\u001b[0m\u001b[1;32m      2\u001b[0m     \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlocation\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m\"/data.yaml\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m25\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0mimgsz\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m640\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0mbatch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m16\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.12/dist-packages/ultralytics/engine/model.py\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(self, trainer, **kwargs)\u001b[0m\n\u001b[1;32m    781\u001b[0m                     \u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"resume\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    782\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 783\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrainer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mtrainer\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_smart_load\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"trainer\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moverrides\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_callbacks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    784\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"resume\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mckpt\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    785\u001b[0m             \u001b[0;31m# Reuse the already-loaded checkpoint model to avoid re-resolving remote weight sources during trainer setup.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.12/dist-packages/ultralytics/models/yolo/detect/train.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, cfg, overrides, _callbacks)\u001b[0m\n\u001b[1;32m     61\u001b[0m             \u001b[0m_callbacks\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mdict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptional\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mDictionary\u001b[0m \u001b[0mof\u001b[0m \u001b[0mcallback\u001b[0m \u001b[0mfunctions\u001b[0m \u001b[0mto\u001b[0m \u001b[0mbe\u001b[0m \u001b[0mexecuted\u001b[0m \u001b[0mduring\u001b[0m \u001b[0mtraining\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     62\u001b[0m         \"\"\"\n\u001b[0;32m---> 63\u001b[0;31m         \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcfg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moverrides\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_callbacks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     64\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     65\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mbuild_dataset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg_path\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"train\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.12/dist-packages/ultralytics/engine/trainer.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, cfg, overrides, _callbacks)\u001b[0m\n\u001b[1;32m    126\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_cfg\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcfg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moverrides\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    127\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcheck_resume\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moverrides\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 128\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdevice\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mselect_device\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    129\u001b[0m         \u001b[0;31m# Update \"-1\" devices so post-training val does not repeat search\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    130\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdevice\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetenv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"CUDA_VISIBLE_DEVICES\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;34m\"cuda\"\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.12/dist-packages/ultralytics/utils/torch_utils.py\u001b[0m in \u001b[0;36mselect_device\u001b[0;34m(device, newline, verbose)\u001b[0m\n\u001b[1;32m    229\u001b[0m                 \u001b[0;32melse\u001b[0m \u001b[0;34m\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    230\u001b[0m             )\n\u001b[0;32m--> 231\u001b[0;31m             raise ValueError(\n\u001b[0m\u001b[1;32m    232\u001b[0m                 \u001b[0;34mf\"Invalid CUDA 'device={device}' requested.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    233\u001b[0m                 \u001b[0;34mf\" Use 'device=cpu' or pass valid CUDA device(s) if available,\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mValueError\u001b[0m: Invalid CUDA 'device=0' requested. Use 'device=cpu' or pass valid CUDA device(s) if available, i.e. 'device=0' or 'device=0,1,2,3' for Multi-GPU.\n\ntorch.cuda.is_available(): False\ntorch.cuda.device_count(): 0\nos.environ['CUDA_VISIBLE_DEVICES']: None\nSee https://pytorch.org/get-started/locally/ for up-to-date torch install instructions if no CUDA devices are seen by torch.\n"]}]},{"cell_type":"code","source":["os.makedirs(\"saved_models\", exist_ok=True)\n","\n","# -----------------------------------\n","# SAVE BEST AND LAST WEIGHTS\n","# -----------------------------------\n","\n","shutil.copy(\n","    \"runs/detect/train-2/weights/best.pt\",\n","    \"saved_models/accident_best.pt\"\n",")\n","\n","shutil.copy(\n","    \"runs/detect/train-2/weights/last.pt\",\n","    \"saved_models/accident_last.pt\"\n",")\n","\n","print(\"Accident Detection Weights Saved Successfully!\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":219},"id":"iuyjU7NRnQe3","executionInfo":{"status":"error","timestamp":1779960965314,"user_tz":-330,"elapsed":33,"user":{"displayName":"Krishna Verma","userId":"18291293214161594665"}},"outputId":"3ebea293-c086-47e0-c60e-3b058d47ca8d"},"execution_count":1,"outputs":[{"output_type":"error","ename":"NameError","evalue":"name 'os' is not defined","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)","\u001b[0;32m/tmp/ipykernel_3908/3895257464.py\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmakedirs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"saved_models\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexist_ok\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;31m# -----------------------------------\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;31m# SAVE BEST AND LAST WEIGHTS\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;31m# -----------------------------------\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mNameError\u001b[0m: name 'os' is not defined"]}]}]}