yolo tensorrt model
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"execution_count": 1,
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"id": "8eb76ac4-c771-46b8-bff0-c6f93dd44658",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2025-09-22T07:50:17.846472Z",
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"iopub.status.busy": "2025-09-22T07:50:17.845771Z",
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"iopub.status.idle": "2025-09-22T07:50:19.023362Z",
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"shell.execute_reply": "2025-09-22T07:50:19.020694Z",
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"outputs": [],
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"source": [
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"!pip list | grep -i ultralytics"
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"execution_count": 2,
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"id": "f5ab5797-3b8b-414b-a618-86c352620da9",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2025-09-22T07:50:26.238547Z",
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"iopub.status.busy": "2025-09-22T07:50:26.237920Z",
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"iopub.status.idle": "2025-09-22T07:50:38.265301Z",
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"shell.execute_reply": "2025-09-22T07:50:38.261814Z",
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"shell.execute_reply.started": "2025-09-22T07:50:26.238489Z"
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"name": "stdout",
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"text": [
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"Collecting ultralytics\n",
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" Downloading ultralytics-8.3.202-py3-none-any.whl.metadata (37 kB)\n",
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"Requirement already satisfied: numpy>=1.23.0 in /home/jovyan/.venv/tf2.20.0-py3.12-cuda12.8/lib/python3.12/site-packages (from ultralytics) (1.26.4)\n",
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"Requirement already satisfied: matplotlib>=3.3.0 in /usr/local/lib/python3.12/dist-packages (from ultralytics) (3.10.6)\n",
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"Requirement already satisfied: opencv-python>=4.6.0 in /usr/local/lib/python3.12/dist-packages (from ultralytics) (4.12.0.88)\n",
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"Requirement already satisfied: torch>=1.8.0 in /home/jovyan/.venv/tf2.20.0-py3.12-cuda12.8/lib/python3.12/site-packages (from ultralytics) (2.8.0)\n",
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"Requirement already satisfied: torchvision>=0.9.0 in /home/jovyan/.venv/tf2.20.0-py3.12-cuda12.8/lib/python3.12/site-packages (from ultralytics) (0.23.0)\n",
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"Requirement already satisfied: psutil in /usr/local/lib/python3.12/dist-packages (from ultralytics) (7.0.0)\n",
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"Collecting polars (from ultralytics)\n",
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" Downloading polars-1.33.1-cp39-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (15 kB)\n",
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"Collecting ultralytics-thop>=2.0.0 (from ultralytics)\n",
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" Downloading ultralytics_thop-2.0.17-py3-none-any.whl.metadata (14 kB)\n",
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"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",
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"Collecting numpy>=1.23.0 (from ultralytics)\n",
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" Downloading numpy-2.2.6-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (62 kB)\n",
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"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",
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"Requirement already satisfied: filelock in /home/jovyan/.venv/tf2.20.0-py3.12-cuda12.8/lib/python3.12/site-packages (from torch>=1.8.0->ultralytics) (3.19.1)\n",
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"Requirement already satisfied: nvidia-cuda-runtime-cu12==12.8.90 in /home/jovyan/.venv/tf2.20.0-py3.12-cuda12.8/lib/python3.12/site-packages (from torch>=1.8.0->ultralytics) (12.8.90)\n",
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"Requirement already satisfied: nvidia-cuda-cupti-cu12==12.8.90 in /home/jovyan/.venv/tf2.20.0-py3.12-cuda12.8/lib/python3.12/site-packages (from torch>=1.8.0->ultralytics) (12.8.90)\n",
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"Requirement already satisfied: nvidia-cudnn-cu12==9.10.2.21 in /home/jovyan/.venv/tf2.20.0-py3.12-cuda12.8/lib/python3.12/site-packages (from torch>=1.8.0->ultralytics) (9.10.2.21)\n",
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"Requirement already satisfied: nvidia-cublas-cu12==12.8.4.1 in /home/jovyan/.venv/tf2.20.0-py3.12-cuda12.8/lib/python3.12/site-packages (from torch>=1.8.0->ultralytics) (12.8.4.1)\n",
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"Requirement already satisfied: nvidia-curand-cu12==10.3.9.90 in /home/jovyan/.venv/tf2.20.0-py3.12-cuda12.8/lib/python3.12/site-packages (from torch>=1.8.0->ultralytics) (10.3.9.90)\n",
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"Requirement already satisfied: nvidia-cusolver-cu12==11.7.3.90 in /home/jovyan/.venv/tf2.20.0-py3.12-cuda12.8/lib/python3.12/site-packages (from torch>=1.8.0->ultralytics) (11.7.3.90)\n",
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"Requirement already satisfied: nvidia-cusparse-cu12==12.5.8.93 in /home/jovyan/.venv/tf2.20.0-py3.12-cuda12.8/lib/python3.12/site-packages (from torch>=1.8.0->ultralytics) (12.5.8.93)\n",
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"Requirement already satisfied: nvidia-cusparselt-cu12==0.7.1 in /home/jovyan/.venv/tf2.20.0-py3.12-cuda12.8/lib/python3.12/site-packages (from torch>=1.8.0->ultralytics) (0.7.1)\n",
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"Requirement already satisfied: nvidia-nvtx-cu12==12.8.90 in /home/jovyan/.venv/tf2.20.0-py3.12-cuda12.8/lib/python3.12/site-packages (from torch>=1.8.0->ultralytics) (12.8.90)\n",
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"Downloading ultralytics-8.3.202-py3-none-any.whl (1.1 MB)\n",
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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.1/1.1 MB\u001b[0m \u001b[31m13.0 MB/s\u001b[0m \u001b[33m0:00:00\u001b[0m\n",
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"\u001b[?25hDownloading numpy-2.2.6-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (16.5 MB)\n",
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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m16.5/16.5 MB\u001b[0m \u001b[31m25.1 MB/s\u001b[0m \u001b[33m0:00:00\u001b[0mm0:00:01\u001b[0m:00:01\u001b[0m\n",
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"\u001b[?25hDownloading ultralytics_thop-2.0.17-py3-none-any.whl (28 kB)\n",
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"Downloading polars-1.33.1-cp39-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (39.7 MB)\n",
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"\u001b[?25hInstalling collected packages: polars, numpy, ultralytics-thop, ultralytics\n",
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"\u001b[2K Attempting uninstall: numpy━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0/4\u001b[0m [polars]\n",
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"\u001b[2K Found existing installation: numpy 1.26.4[0m \u001b[32m0/4\u001b[0m [polars]\n",
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"\u001b[2K Uninstalling numpy-1.26.4:0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1/4\u001b[0m [numpy]\n",
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"\u001b[2K Successfully uninstalled numpy-1.26.4━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1/4\u001b[0m [numpy]\n",
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"\u001b[1A\u001b[2KSuccessfully installed numpy-2.2.6 polars-1.33.1 ultralytics-8.3.202 ultralytics-thop-2.0.17\n"
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"!pip install ultralytics"
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"iopub.status.idle": "2025-09-22T07:51:11.134155Z",
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"text": [
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"Creating new Ultralytics Settings v0.0.6 file ✅ \n",
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"View Ultralytics Settings with 'yolo settings' or at '/home/jovyan/.config/Ultralytics/settings.json'\n",
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"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"
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],
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"source": [
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"from ultralytics import YOLO"
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"text": [
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"\u001b[KDownloading https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n.pt to 'yolo11n.pt': 100% ━━━━━━━━━━━━ 5.4MB 99.0MB/s 0.1ss\n"
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"source": [
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"# Load the YOLO11 model\n",
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"model = YOLO(\"yolo11n.pt\")"
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]
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"iopub.execute_input": "2025-09-22T07:51:21.848608Z",
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"iopub.status.idle": "2025-09-22T07:53:42.507589Z",
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"shell.execute_reply": "2025-09-22T07:53:42.505881Z",
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"shell.execute_reply.started": "2025-09-22T07:51:21.848546Z"
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}
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},
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{
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"name": "stdout",
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"text": [
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"WARNING ⚠️ TensorRT requires GPU export, automatically assigning device=0\n",
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"Ultralytics 8.3.202 🚀 Python-3.12.3 torch-2.8.0+cu128 CUDA:0 (NVIDIA GeForce RTX 2080 Ti, 10823MiB)\n",
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"💡 ProTip: Export to OpenVINO format for best performance on Intel hardware. Learn more at https://docs.ultralytics.com/integrations/openvino/\n",
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"YOLO11n summary (fused): 100 layers, 2,616,248 parameters, 0 gradients, 6.5 GFLOPs\n",
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"\n",
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"\u001b[34m\u001b[1mPyTorch:\u001b[0m starting from 'yolo11n.pt' with input shape (1, 3, 640, 640) BCHW and output shape(s) (1, 84, 8400) (5.4 MB)\n",
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"\u001b[31m\u001b[1mrequirements:\u001b[0m Ultralytics requirements ['onnx>=1.12.0', 'onnxslim>=0.1.67', 'onnxruntime-gpu'] not found, attempting AutoUpdate...\n",
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"Collecting onnx>=1.12.0\n",
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" Downloading onnx-1.19.0-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.metadata (7.0 kB)\n",
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" Downloading onnxruntime_gpu-1.22.0-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl.metadata (5.1 kB)\n",
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"\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/18.2 MB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\n",
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"\u001b[2K \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m \u001b[32m210.2/283.2 MB\u001b[0m \u001b[31m39.0 MB/s\u001b[0m eta \u001b[36m0:00:02\u001b[0m\n",
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"\u001b[2K \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m \u001b[32m218.1/283.2 MB\u001b[0m \u001b[31m38.2 MB/s\u001b[0m eta \u001b[36m0:00:02\u001b[0m\n",
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"\u001b[2K \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━\u001b[0m \u001b[32m221.8/283.2 MB\u001b[0m \u001b[31m37.2 MB/s\u001b[0m eta \u001b[36m0:00:02\u001b[0m\n",
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"\u001b[2K \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━\u001b[0m \u001b[32m232.0/283.2 MB\u001b[0m \u001b[31m38.2 MB/s\u001b[0m eta \u001b[36m0:00:02\u001b[0m\n",
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"\u001b[2K \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━\u001b[0m \u001b[32m237.8/283.2 MB\u001b[0m \u001b[31m37.3 MB/s\u001b[0m eta \u001b[36m0:00:02\u001b[0m\n",
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"\u001b[2K \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━\u001b[0m \u001b[32m243.8/283.2 MB\u001b[0m \u001b[31m37.1 MB/s\u001b[0m eta \u001b[36m0:00:02\u001b[0m\n",
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||||||
"\u001b[2K \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━\u001b[0m \u001b[32m250.3/283.2 MB\u001b[0m \u001b[31m37.7 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\n",
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||||||
"\u001b[2K \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━\u001b[0m \u001b[32m258.5/283.2 MB\u001b[0m \u001b[31m37.0 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\n",
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"\u001b[2K \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━\u001b[0m \u001b[32m263.5/283.2 MB\u001b[0m \u001b[31m36.3 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\n",
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||||||
"\u001b[2K \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━\u001b[0m \u001b[32m270.5/283.2 MB\u001b[0m \u001b[31m35.8 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\n",
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|
||||||
"\u001b[2K \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m \u001b[32m277.6/283.2 MB\u001b[0m \u001b[31m35.0 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\n",
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||||||
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m283.2/283.2 MB\u001b[0m \u001b[31m35.9 MB/s\u001b[0m \u001b[33m0:00:07\u001b[0m\n",
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|
||||||
"\u001b[?25hDownloading coloredlogs-15.0.1-py2.py3-none-any.whl (46 kB)\n",
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||||||
"Downloading humanfriendly-10.0-py2.py3-none-any.whl (86 kB)\n",
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||||||
"Installing collected packages: humanfriendly, onnx, coloredlogs, onnxslim, onnxruntime-gpu\n",
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||||||
"\u001b[?25l\n",
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"\u001b[2K \u001b[91m━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1/5\u001b[0m [onnx]\n",
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||||||
"\u001b[2K \u001b[91m━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1/5\u001b[0m [onnx]\n",
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||||||
"\u001b[2K \u001b[91m━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1/5\u001b[0m [onnx]\n",
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||||||
"\u001b[2K \u001b[91m━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1/5\u001b[0m [onnx]\n",
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|
||||||
"\u001b[2K \u001b[91m━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1/5\u001b[0m [onnx]\n",
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||||||
"\u001b[2K \u001b[91m━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1/5\u001b[0m [onnx]\n",
|
|
||||||
"\u001b[2K \u001b[91m━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1/5\u001b[0m [onnx]\n",
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|
||||||
"\u001b[2K \u001b[91m━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1/5\u001b[0m [onnx]\n",
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||||||
"\u001b[2K \u001b[91m━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1/5\u001b[0m [onnx]\n",
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||||||
"\u001b[2K \u001b[91m━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1/5\u001b[0m [onnx]\n",
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"\u001b[2K \u001b[91m━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1/5\u001b[0m [onnx]\n",
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||||||
"\u001b[2K \u001b[91m━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1/5\u001b[0m [onnx]\n",
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"\u001b[2K \u001b[91m━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1/5\u001b[0m [onnx]\n",
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||||||
"\u001b[2K \u001b[91m━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1/5\u001b[0m [onnx]\n",
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"\u001b[2K \u001b[91m━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1/5\u001b[0m [onnx]\n",
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||||||
"\u001b[2K \u001b[91m━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1/5\u001b[0m [onnx]\n",
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||||||
"\u001b[2K \u001b[91m━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1/5\u001b[0m [onnx]\n",
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||||||
"\u001b[2K \u001b[91m━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1/5\u001b[0m [onnx]\n",
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||||||
"\u001b[2K \u001b[91m━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1/5\u001b[0m [onnx]\n",
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||||||
"\u001b[2K \u001b[91m━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1/5\u001b[0m [onnx]\n",
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||||||
"\u001b[2K \u001b[91m━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1/5\u001b[0m [onnx]\n",
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||||||
"\u001b[2K \u001b[91m━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1/5\u001b[0m [onnx]\n",
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||||||
"\u001b[2K \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3/5\u001b[0m [onnxslim]\n",
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"\u001b[2K \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━\u001b[0m \u001b[32m4/5\u001b[0m [onnxruntime-gpu]\n",
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"\u001b[2K \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━\u001b[0m \u001b[32m4/5\u001b[0m [onnxruntime-gpu]\n",
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||||||
"\u001b[2K \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━\u001b[0m \u001b[32m4/5\u001b[0m [onnxruntime-gpu]\n",
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|
||||||
"\u001b[2K \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━\u001b[0m \u001b[32m4/5\u001b[0m [onnxruntime-gpu]\n",
|
|
||||||
"\u001b[2K \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━\u001b[0m \u001b[32m4/5\u001b[0m [onnxruntime-gpu]\n",
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|
||||||
"\u001b[2K \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━\u001b[0m \u001b[32m4/5\u001b[0m [onnxruntime-gpu]\n",
|
|
||||||
"\u001b[2K \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━\u001b[0m \u001b[32m4/5\u001b[0m [onnxruntime-gpu]\n",
|
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||||||
"\u001b[2K \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━\u001b[0m \u001b[32m4/5\u001b[0m [onnxruntime-gpu]\n",
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|
||||||
"\u001b[2K \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━\u001b[0m \u001b[32m4/5\u001b[0m [onnxruntime-gpu]\n",
|
|
||||||
"\u001b[2K \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━\u001b[0m \u001b[32m4/5\u001b[0m [onnxruntime-gpu]\n",
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|
||||||
"\u001b[2K \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━\u001b[0m \u001b[32m4/5\u001b[0m [onnxruntime-gpu]\n",
|
|
||||||
"\u001b[2K \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━\u001b[0m \u001b[32m4/5\u001b[0m [onnxruntime-gpu]\n",
|
|
||||||
"\u001b[2K \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━\u001b[0m \u001b[32m4/5\u001b[0m [onnxruntime-gpu]\n",
|
|
||||||
"\u001b[2K \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━\u001b[0m \u001b[32m4/5\u001b[0m [onnxruntime-gpu]\n",
|
|
||||||
"\u001b[2K \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━\u001b[0m \u001b[32m4/5\u001b[0m [onnxruntime-gpu]\n",
|
|
||||||
"\u001b[2K \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━\u001b[0m \u001b[32m4/5\u001b[0m [onnxruntime-gpu]\n",
|
|
||||||
"\u001b[2K \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━\u001b[0m \u001b[32m4/5\u001b[0m [onnxruntime-gpu]\n",
|
|
||||||
"\u001b[2K \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━\u001b[0m \u001b[32m4/5\u001b[0m [onnxruntime-gpu]\n",
|
|
||||||
"\u001b[2K \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━\u001b[0m \u001b[32m4/5\u001b[0m [onnxruntime-gpu]\n",
|
|
||||||
"\u001b[2K \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━\u001b[0m \u001b[32m4/5\u001b[0m [onnxruntime-gpu]\n",
|
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||||||
"\u001b[2K \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━\u001b[0m \u001b[32m4/5\u001b[0m [onnxruntime-gpu]\n",
|
|
||||||
"\u001b[2K \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━\u001b[0m \u001b[32m4/5\u001b[0m [onnxruntime-gpu]\n",
|
|
||||||
"\u001b[2K \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━\u001b[0m \u001b[32m4/5\u001b[0m [onnxruntime-gpu]\n",
|
|
||||||
"\u001b[2K \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━\u001b[0m \u001b[32m4/5\u001b[0m [onnxruntime-gpu]\n",
|
|
||||||
"\u001b[2K \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━\u001b[0m \u001b[32m4/5\u001b[0m [onnxruntime-gpu]\n",
|
|
||||||
"\u001b[2K \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━\u001b[0m \u001b[32m4/5\u001b[0m [onnxruntime-gpu]\n",
|
|
||||||
"\u001b[2K \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━\u001b[0m \u001b[32m4/5\u001b[0m [onnxruntime-gpu]\n",
|
|
||||||
"\u001b[2K \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━\u001b[0m \u001b[32m4/5\u001b[0m [onnxruntime-gpu]\n",
|
|
||||||
"\u001b[2K \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━\u001b[0m \u001b[32m4/5\u001b[0m [onnxruntime-gpu]\n",
|
|
||||||
"\u001b[2K \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━\u001b[0m \u001b[32m4/5\u001b[0m [onnxruntime-gpu]\n",
|
|
||||||
"\u001b[2K \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━\u001b[0m \u001b[32m4/5\u001b[0m [onnxruntime-gpu]\n",
|
|
||||||
"\u001b[2K \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━\u001b[0m \u001b[32m4/5\u001b[0m [onnxruntime-gpu]\n",
|
|
||||||
"\u001b[2K \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━\u001b[0m \u001b[32m4/5\u001b[0m [onnxruntime-gpu]\n",
|
|
||||||
"\u001b[2K \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━\u001b[0m \u001b[32m4/5\u001b[0m [onnxruntime-gpu]\n",
|
|
||||||
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m5/5\u001b[0m [onnxruntime-gpu]\n",
|
|
||||||
"\u001b[?25h\n",
|
|
||||||
"\u001b[1A\u001b[2KSuccessfully installed coloredlogs-15.0.1 humanfriendly-10.0 onnx-1.19.0 onnxruntime-gpu-1.22.0 onnxslim-0.1.69\n",
|
|
||||||
"\n",
|
|
||||||
"\u001b[31m\u001b[1mrequirements:\u001b[0m AutoUpdate success ✅ 21.9s\n",
|
|
||||||
"WARNING ⚠️ \u001b[31m\u001b[1mrequirements:\u001b[0m \u001b[1mRestart runtime or rerun command for updates to take effect\u001b[0m\n",
|
|
||||||
"\n",
|
|
||||||
"\n",
|
|
||||||
"\u001b[34m\u001b[1mONNX:\u001b[0m starting export with onnx 1.19.0 opset 19...\n",
|
|
||||||
"\u001b[34m\u001b[1mONNX:\u001b[0m slimming with onnxslim 0.1.69...\n",
|
|
||||||
"\u001b[34m\u001b[1mONNX:\u001b[0m export success ✅ 24.2s, saved as 'yolo11n.onnx' (10.2 MB)\n",
|
|
||||||
"\n",
|
|
||||||
"\u001b[34m\u001b[1mTensorRT:\u001b[0m starting export with TensorRT 10.9.0.34...\n",
|
|
||||||
"[09/22/2025-07:51:47] [TRT] [I] [MemUsageChange] Init CUDA: CPU -2, GPU +0, now: CPU 693, GPU 314 (MiB)\n",
|
|
||||||
"[09/22/2025-07:51:50] [TRT] [I] [MemUsageChange] Init builder kernel library: CPU +1141, GPU +192, now: CPU 2024, GPU 506 (MiB)\n",
|
|
||||||
"[09/22/2025-07:51:50] [TRT] [I] ----------------------------------------------------------------\n",
|
|
||||||
"[09/22/2025-07:51:50] [TRT] [I] Input filename: yolo11n.onnx\n",
|
|
||||||
"[09/22/2025-07:51:50] [TRT] [I] ONNX IR version: 0.0.9\n",
|
|
||||||
"[09/22/2025-07:51:50] [TRT] [I] Opset version: 19\n",
|
|
||||||
"[09/22/2025-07:51:50] [TRT] [I] Producer name: pytorch\n",
|
|
||||||
"[09/22/2025-07:51:50] [TRT] [I] Producer version: 2.8.0\n",
|
|
||||||
"[09/22/2025-07:51:50] [TRT] [I] Domain: \n",
|
|
||||||
"[09/22/2025-07:51:50] [TRT] [I] Model version: 0\n",
|
|
||||||
"[09/22/2025-07:51:50] [TRT] [I] Doc string: \n",
|
|
||||||
"[09/22/2025-07:51:50] [TRT] [I] ----------------------------------------------------------------\n",
|
|
||||||
"\u001b[34m\u001b[1mTensorRT:\u001b[0m input \"images\" with shape(1, 3, 640, 640) DataType.FLOAT\n",
|
|
||||||
"\u001b[34m\u001b[1mTensorRT:\u001b[0m output \"output0\" with shape(1, 84, 8400) DataType.FLOAT\n",
|
|
||||||
"\u001b[34m\u001b[1mTensorRT:\u001b[0m building FP32 engine as yolo11n.engine\n",
|
|
||||||
"[09/22/2025-07:51:50] [TRT] [I] BuilderFlag::kTF32 is set but hardware does not support TF32. Disabling TF32.\n",
|
|
||||||
"[09/22/2025-07:51:50] [TRT] [I] BuilderFlag::kTF32 is set but hardware does not support TF32. Disabling TF32.\n",
|
|
||||||
"[09/22/2025-07:51:50] [TRT] [I] Local timing cache in use. Profiling results in this builder pass will not be stored.\n",
|
|
||||||
"[09/22/2025-07:52:45] [TRT] [I] Compiler backend is used during engine build.\n",
|
|
||||||
"[09/22/2025-07:53:41] [TRT] [I] Detected 1 inputs and 1 output network tensors.\n",
|
|
||||||
"[09/22/2025-07:53:41] [TRT] [I] Total Host Persistent Memory: 423184 bytes\n",
|
|
||||||
"[09/22/2025-07:53:41] [TRT] [I] Total Device Persistent Memory: 711680 bytes\n",
|
|
||||||
"[09/22/2025-07:53:41] [TRT] [I] Max Scratch Memory: 2764800 bytes\n",
|
|
||||||
"[09/22/2025-07:53:41] [TRT] [I] [BlockAssignment] Started assigning block shifts. This will take 299 steps to complete.\n",
|
|
||||||
"[09/22/2025-07:53:41] [TRT] [I] [BlockAssignment] Algorithm ShiftNTopDown took 43.6829ms to assign 11 blocks to 299 nodes requiring 19252224 bytes.\n",
|
|
||||||
"[09/22/2025-07:53:41] [TRT] [I] Total Activation Memory: 19251200 bytes\n",
|
|
||||||
"[09/22/2025-07:53:41] [TRT] [I] Total Weights Memory: 11513924 bytes\n",
|
|
||||||
"[09/22/2025-07:53:42] [TRT] [I] Compiler backend is used during engine execution.\n",
|
|
||||||
"[09/22/2025-07:53:42] [TRT] [I] Engine generation completed in 111.239 seconds.\n",
|
|
||||||
"[09/22/2025-07:53:42] [TRT] [I] [MemUsageStats] Peak memory usage of TRT CPU/GPU memory allocators: CPU 1 MiB, GPU 261 MiB\n",
|
|
||||||
"\u001b[34m\u001b[1mTensorRT:\u001b[0m export success ✅ 139.2s, saved as 'yolo11n.engine' (13.2 MB)\n",
|
|
||||||
"\n",
|
|
||||||
"Export complete (140.6s)\n",
|
|
||||||
"Results saved to \u001b[1m/home/jovyan/model/groupuser/yolo11-tensorrt\u001b[0m\n",
|
|
||||||
"Predict: yolo predict task=detect model=yolo11n.engine imgsz=640 \n",
|
|
||||||
"Validate: yolo val task=detect model=yolo11n.engine imgsz=640 data=/usr/src/ultralytics/ultralytics/cfg/datasets/coco.yaml \n",
|
|
||||||
"Visualize: https://netron.app\n"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"data": {
|
|
||||||
"text/plain": [
|
|
||||||
"'yolo11n.engine'"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"execution_count": 6,
|
|
||||||
"metadata": {},
|
|
||||||
"output_type": "execute_result"
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"source": [
|
|
||||||
"# Export the model to TensorRT format\n",
|
|
||||||
"model.export(format=\"engine\") # creates 'yolo11n.engine'"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": 8,
|
|
||||||
"id": "18eecf5a-3cdc-4d84-82db-545eebffc523",
|
|
||||||
"metadata": {
|
|
||||||
"execution": {
|
|
||||||
"iopub.execute_input": "2025-09-22T07:53:54.847090Z",
|
|
||||||
"iopub.status.busy": "2025-09-22T07:53:54.846001Z",
|
|
||||||
"iopub.status.idle": "2025-09-22T07:53:54.860323Z",
|
|
||||||
"shell.execute_reply": "2025-09-22T07:53:54.859102Z",
|
|
||||||
"shell.execute_reply.started": "2025-09-22T07:53:54.846996Z"
|
|
||||||
}
|
|
||||||
},
|
|
||||||
"outputs": [
|
|
||||||
{
|
|
||||||
"name": "stdout",
|
|
||||||
"output_type": "stream",
|
|
||||||
"text": [
|
|
||||||
"WARNING ⚠️ Unable to automatically guess model task, assuming 'task=detect'. Explicitly define task for your model, i.e. 'task=detect', 'segment', 'classify','pose' or 'obb'.\n"
|
|
||||||
]
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"source": [
|
|
||||||
"# Load the exported TensorRT model\n",
|
|
||||||
"tensorrt_model = YOLO(\"yolo11n.engine\")"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": null,
|
|
||||||
"id": "4cd3752f-3ed6-4d73-9394-9c04ae4d057f",
|
|
||||||
"metadata": {},
|
|
||||||
"outputs": [],
|
|
||||||
"source": []
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"metadata": {
|
|
||||||
"kernelspec": {
|
|
||||||
"display_name": "tf2.20.0-py3.12-cuda12.8",
|
|
||||||
"language": "python",
|
|
||||||
"name": "tf2.20.0-py3.12-cuda12.8"
|
|
||||||
},
|
|
||||||
"language_info": {
|
|
||||||
"codemirror_mode": {
|
|
||||||
"name": "ipython",
|
|
||||||
"version": 3
|
|
||||||
},
|
|
||||||
"file_extension": ".py",
|
|
||||||
"mimetype": "text/x-python",
|
|
||||||
"name": "python",
|
|
||||||
"nbconvert_exporter": "python",
|
|
||||||
"pygments_lexer": "ipython3",
|
|
||||||
"version": "3.12.3"
|
|
||||||
}
|
|
||||||
},
|
|
||||||
"nbformat": 4,
|
|
||||||
"nbformat_minor": 5
|
|
||||||
}
|
|
||||||
BIN
yolo11n.onnx
(Stored with Git LFS)
BIN
yolo11n.onnx
(Stored with Git LFS)
Binary file not shown.
BIN
yolo11n.pt
(Stored with Git LFS)
BIN
yolo11n.pt
(Stored with Git LFS)
Binary file not shown.
Loading…
Reference in New Issue
Block a user