MkDocs validation and Export internal linking (#10368)
Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
This commit is contained in:
Glenn Jocher
GitHub
parent
8558bec402
commit
632c906578
22 changed files with 289 additions and 279 deletions
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@ -25,11 +25,13 @@
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" <a href=\"https://ultralytics.com/yolov8\" target=\"_blank\">\n",
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" <img width=\"1024\", src=\"https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/banner-yolov8.png\"></a>\n",
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"\n",
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" [中文](https://docs.ultralytics.com/zh/) | [한국어](https://docs.ultralytics.com/ko/) | [日本語](https://docs.ultralytics.com/ja/) | [Русский](https://docs.ultralytics.com/ru/) | [Deutsch](https://docs.ultralytics.com/de/) | [Français](https://docs.ultralytics.com/fr/) | [Español](https://docs.ultralytics.com/es/) | [Português](https://docs.ultralytics.com/pt/) | [हिन्दी](https://docs.ultralytics.com/hi/) | [العربية](https://docs.ultralytics.com/ar/)\n",
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" [中文](https://docs.ultralytics.com/zh/) | [한국어](https://docs.ultralytics.com/ko/) | [日本語](https://docs.ultralytics.com/ja/) | [Русский](https://docs.ultralytics.com/ru/) | [Deutsch](https://docs.ultralytics.com/de/) | [Français](https://docs.ultralytics.com/fr/) | [Español](https://docs.ultralytics.com/es/) | [Português](https://docs.ultralytics.com/pt/) | [Türkçe](https://docs.ultralytics.com/tr/) | [Tiếng Việt](https://docs.ultralytics.com/vi/) | [हिन्दी](https://docs.ultralytics.com/hi/) | [العربية](https://docs.ultralytics.com/ar/)\n",
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"\n",
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" <a href=\"https://github.com/ultralytics/ultralytics/actions/workflows/ci.yaml\"><img src=\"https://github.com/ultralytics/ultralytics/actions/workflows/ci.yaml/badge.svg\" alt=\"Ultralytics CI\"></a>\n",
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" <a href=\"https://console.paperspace.com/github/ultralytics/ultralytics\"><img src=\"https://assets.paperspace.io/img/gradient-badge.svg\" alt=\"Run on Gradient\"/></a>\n",
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" <a href=\"https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/examples/tutorial.ipynb\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
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" <a href=\"https://www.kaggle.com/ultralytics/yolov8\"><img src=\"https://kaggle.com/static/images/open-in-kaggle.svg\" alt=\"Open In Kaggle\"></a>\n",
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" <a href=\"https://ultralytics.com/discord\"><img alt=\"Discord\" src=\"https://img.shields.io/discord/1089800235347353640?logo=discord&logoColor=white&label=Discord&color=blue\"></a>\n",
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"\n",
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"Welcome to the Ultralytics YOLOv8 🚀 notebook! <a href=\"https://github.com/ultralytics/ultralytics\">YOLOv8</a> is the latest version of the YOLO (You Only Look Once) AI models developed by <a href=\"https://ultralytics.com\">Ultralytics</a>. This notebook serves as the starting point for exploring the various resources available to help you get started with YOLOv8 and understand its features and capabilities.\n",
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"\n",
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@ -58,7 +60,7 @@
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"outputId": "51d15672-e688-4fb8-d9d0-00d1916d3532"
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"outputId": "96335d4c-20a9-4864-f7a4-bb2eb0077a9d"
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},
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"source": [
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"%pip install ultralytics\n",
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@ -71,8 +73,8 @@
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"Ultralytics YOLOv8.1.23 🚀 Python-3.10.12 torch-2.1.0+cu121 CUDA:0 (Tesla T4, 15102MiB)\n",
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"Setup complete ✅ (2 CPUs, 12.7 GB RAM, 26.3/78.2 GB disk)\n"
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"Ultralytics YOLOv8.2.3 🚀 Python-3.10.12 torch-2.2.1+cu121 CUDA:0 (Tesla T4, 15102MiB)\n",
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"Setup complete ✅ (2 CPUs, 12.7 GB RAM, 28.8/78.2 GB disk)\n"
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]
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}
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]
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@ -95,7 +97,7 @@
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"outputId": "37738db7-4284-47de-b3ed-b82f2431ed23"
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"outputId": "84f32db2-80b0-4f35-9a2a-a56d11f7863f"
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},
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"source": [
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"# Run inference on an image with YOLOv8n\n",
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@ -108,14 +110,14 @@
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"name": "stdout",
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"text": [
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"Downloading https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8n.pt to 'yolov8n.pt'...\n",
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"100% 6.23M/6.23M [00:00<00:00, 72.6MB/s]\n",
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"Ultralytics YOLOv8.1.23 🚀 Python-3.10.12 torch-2.1.0+cu121 CUDA:0 (Tesla T4, 15102MiB)\n",
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"100% 6.23M/6.23M [00:00<00:00, 83.2MB/s]\n",
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"Ultralytics YOLOv8.2.3 🚀 Python-3.10.12 torch-2.2.1+cu121 CUDA:0 (Tesla T4, 15102MiB)\n",
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"YOLOv8n summary (fused): 168 layers, 3151904 parameters, 0 gradients, 8.7 GFLOPs\n",
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"\n",
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"Downloading https://ultralytics.com/images/zidane.jpg to 'zidane.jpg'...\n",
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"100% 165k/165k [00:00<00:00, 7.05MB/s]\n",
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"image 1/1 /content/zidane.jpg: 384x640 2 persons, 1 tie, 162.0ms\n",
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"Speed: 13.9ms preprocess, 162.0ms inference, 1259.5ms postprocess per image at shape (1, 3, 384, 640)\n",
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"100% 165k/165k [00:00<00:00, 11.1MB/s]\n",
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"image 1/1 /content/zidane.jpg: 384x640 2 persons, 1 tie, 21.4ms\n",
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"Speed: 1.9ms preprocess, 21.4ms inference, 6.2ms postprocess per image at shape (1, 3, 384, 640)\n",
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"Results saved to \u001b[1mruns/detect/predict\u001b[0m\n",
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"💡 Learn more at https://docs.ultralytics.com/modes/predict\n"
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]
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@ -160,7 +162,7 @@
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"cell_type": "code",
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"metadata": {
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"id": "X58w8JLpMnjH",
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"outputId": "61001937-ccd2-4157-a373-156a57495231",
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"outputId": "bed10d45-ceb6-4b6f-86b7-9428208b142a",
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"colab": {
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"base_uri": "https://localhost:8080/"
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}
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@ -175,20 +177,20 @@
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"Ultralytics YOLOv8.1.23 🚀 Python-3.10.12 torch-2.1.0+cu121 CUDA:0 (Tesla T4, 15102MiB)\n",
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"Ultralytics YOLOv8.2.3 🚀 Python-3.10.12 torch-2.2.1+cu121 CUDA:0 (Tesla T4, 15102MiB)\n",
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"YOLOv8n summary (fused): 168 layers, 3151904 parameters, 0 gradients, 8.7 GFLOPs\n",
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"\n",
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"Dataset 'coco8.yaml' images not found ⚠️, missing path '/content/datasets/coco8/images/val'\n",
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"Downloading https://ultralytics.com/assets/coco8.zip to '/content/datasets/coco8.zip'...\n",
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"100% 433k/433k [00:00<00:00, 12.5MB/s]\n",
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"Unzipping /content/datasets/coco8.zip to /content/datasets/coco8...: 100% 25/25 [00:00<00:00, 4546.38file/s]\n",
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"Dataset download success ✅ (0.9s), saved to \u001b[1m/content/datasets\u001b[0m\n",
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"100% 433k/433k [00:00<00:00, 14.2MB/s]\n",
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"Unzipping /content/datasets/coco8.zip to /content/datasets/coco8...: 100% 25/25 [00:00<00:00, 1093.93file/s]\n",
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"Dataset download success ✅ (1.3s), saved to \u001b[1m/content/datasets\u001b[0m\n",
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"\n",
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"Downloading https://ultralytics.com/assets/Arial.ttf to '/root/.config/Ultralytics/Arial.ttf'...\n",
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"100% 755k/755k [00:00<00:00, 17.8MB/s]\n",
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"\u001b[34m\u001b[1mval: \u001b[0mScanning /content/datasets/coco8/labels/val... 4 images, 0 backgrounds, 0 corrupt: 100% 4/4 [00:00<00:00, 275.94it/s]\n",
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"100% 755k/755k [00:00<00:00, 17.4MB/s]\n",
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"\u001b[34m\u001b[1mval: \u001b[0mScanning /content/datasets/coco8/labels/val... 4 images, 0 backgrounds, 0 corrupt: 100% 4/4 [00:00<00:00, 157.00it/s]\n",
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"\u001b[34m\u001b[1mval: \u001b[0mNew cache created: /content/datasets/coco8/labels/val.cache\n",
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" Class Images Instances Box(P R mAP50 mAP50-95): 100% 1/1 [00:02<00:00, 2.23s/it]\n",
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" Class Images Instances Box(P R mAP50 mAP50-95): 100% 1/1 [00:06<00:00, 6.89s/it]\n",
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" all 4 17 0.621 0.833 0.888 0.63\n",
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" person 4 10 0.721 0.5 0.519 0.269\n",
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" dog 4 1 0.37 1 0.995 0.597\n",
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" elephant 4 2 0.505 0.5 0.828 0.394\n",
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" umbrella 4 1 0.564 1 0.995 0.995\n",
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" potted plant 4 1 0.814 1 0.995 0.895\n",
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"Speed: 0.3ms preprocess, 56.9ms inference, 0.0ms loss, 222.8ms postprocess per image\n",
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"Speed: 0.3ms preprocess, 4.9ms inference, 0.0ms loss, 1.3ms postprocess per image\n",
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"Results saved to \u001b[1mruns/detect/val\u001b[0m\n",
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"💡 Learn more at https://docs.ultralytics.com/modes/val\n"
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]
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@ -239,7 +241,7 @@
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"cell_type": "code",
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"metadata": {
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"id": "1NcFxRcFdJ_O",
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"outputId": "1ec62d53-41eb-444f-e2f7-cef5c18b9a27",
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"outputId": "9f60c6cb-fa9c-4785-cb7a-71d40abeaf38",
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"colab": {
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"base_uri": "https://localhost:8080/"
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}
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@ -254,8 +256,11 @@
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"Ultralytics YOLOv8.1.23 🚀 Python-3.10.12 torch-2.1.0+cu121 CUDA:0 (Tesla T4, 15102MiB)\n",
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"\u001b[34m\u001b[1mengine/trainer: \u001b[0mtask=detect, mode=train, model=yolov8n.pt, data=coco8.yaml, epochs=3, time=None, patience=100, batch=16, imgsz=640, save=True, save_period=-1, cache=False, device=None, workers=8, project=None, name=train, exist_ok=False, pretrained=True, optimizer=auto, verbose=True, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, amp=True, fraction=1.0, profile=False, freeze=None, multi_scale=False, overlap_mask=True, mask_ratio=4, dropout=0.0, val=True, split=val, save_json=False, save_hybrid=False, conf=None, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=None, vid_stride=1, stream_buffer=False, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, embed=None, show=False, save_frames=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, show_boxes=True, line_width=None, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=False, opset=None, workspace=4, nms=False, lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=7.5, cls=0.5, dfl=1.5, pose=12.0, kobj=1.0, label_smoothing=0.0, nbs=64, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0, auto_augment=randaugment, erasing=0.4, crop_fraction=1.0, cfg=None, tracker=botsort.yaml, save_dir=runs/detect/train\n",
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"Ultralytics YOLOv8.2.3 🚀 Python-3.10.12 torch-2.2.1+cu121 CUDA:0 (Tesla T4, 15102MiB)\n",
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"\u001b[34m\u001b[1mengine/trainer: \u001b[0mtask=detect, mode=train, model=yolov8n.pt, data=coco8.yaml, epochs=3, time=None, patience=100, batch=16, imgsz=640, save=True, save_period=-1, cache=False, device=None, workers=8, project=None, name=train, exist_ok=False, pretrained=True, optimizer=auto, verbose=True, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, amp=True, fraction=1.0, profile=False, freeze=None, multi_scale=False, overlap_mask=True, mask_ratio=4, dropout=0.0, val=True, split=val, save_json=False, save_hybrid=False, conf=None, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=None, vid_stride=1, stream_buffer=False, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, embed=None, show=False, save_frames=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, show_boxes=True, line_width=None, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=False, opset=None, workspace=4, nms=False, lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=7.5, cls=0.5, dfl=1.5, pose=12.0, kobj=1.0, label_smoothing=0.0, nbs=64, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, bgr=0.0, mosaic=1.0, mixup=0.0, copy_paste=0.0, auto_augment=randaugment, erasing=0.4, crop_fraction=1.0, cfg=None, tracker=botsort.yaml, save_dir=runs/detect/train\n",
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"2024-04-27 18:41:11.160690: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
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"2024-04-27 18:41:11.160751: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
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"2024-04-27 18:41:11.162138: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
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"\n",
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" from n params module arguments \n",
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" 0 -1 1 464 ultralytics.nn.modules.conv.Conv [3, 16, 3, 2] \n",
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"Freezing layer 'model.22.dfl.conv.weight'\n",
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"\u001b[34m\u001b[1mAMP: \u001b[0mrunning Automatic Mixed Precision (AMP) checks with YOLOv8n...\n",
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"\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n",
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"\u001b[34m\u001b[1mtrain: \u001b[0mScanning /content/datasets/coco8/labels/train... 4 images, 0 backgrounds, 0 corrupt: 100% 4/4 [00:00<00:00, 43351.98it/s]\n",
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"\u001b[34m\u001b[1mtrain: \u001b[0mScanning /content/datasets/coco8/labels/train... 4 images, 0 backgrounds, 0 corrupt: 100% 4/4 [00:00<00:00, 837.19it/s]\n",
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"\u001b[34m\u001b[1mtrain: \u001b[0mNew cache created: /content/datasets/coco8/labels/train.cache\n",
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"\u001b[34m\u001b[1malbumentations: \u001b[0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8))\n",
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"/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n",
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" self.pid = os.fork()\n",
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"\u001b[34m\u001b[1mval: \u001b[0mScanning /content/datasets/coco8/labels/val.cache... 4 images, 0 backgrounds, 0 corrupt: 100% 4/4 [00:00<?, ?it/s]\n",
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"Plotting labels to runs/detect/train/labels.jpg... \n",
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"\u001b[34m\u001b[1moptimizer:\u001b[0m 'optimizer=auto' found, ignoring 'lr0=0.01' and 'momentum=0.937' and determining best 'optimizer', 'lr0' and 'momentum' automatically... \n",
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"Starting training for 3 epochs...\n",
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"\n",
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" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
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" 1/3 0.77G 0.9308 3.155 1.291 32 640: 100% 1/1 [00:01<00:00, 1.70s/it]\n",
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" Class Images Instances Box(P R mAP50 mAP50-95): 100% 1/1 [00:00<00:00, 1.90it/s]\n",
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" all 4 17 0.858 0.54 0.726 0.51\n",
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" 1/3 0.81G 1.039 3.146 1.498 25 640: 100% 1/1 [00:01<00:00, 1.51s/it]\n",
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" Class Images Instances Box(P R mAP50 mAP50-95): 100% 1/1 [00:00<00:00, 2.32it/s]\n",
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" all 4 17 0.62 0.885 0.888 0.621\n",
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"\n",
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" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
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" 2/3 0.78G 1.162 3.127 1.518 33 640: 100% 1/1 [00:00<00:00, 8.18it/s]\n",
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" Class Images Instances Box(P R mAP50 mAP50-95): 100% 1/1 [00:00<00:00, 3.71it/s]\n",
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" all 4 17 0.904 0.526 0.742 0.5\n",
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" 2/3 0.772G 1.169 2.779 1.442 36 640: 100% 1/1 [00:00<00:00, 8.14it/s]\n",
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" Class Images Instances Box(P R mAP50 mAP50-95): 100% 1/1 [00:00<00:00, 3.22it/s]\n",
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" all 4 17 0.595 0.903 0.888 0.616\n",
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"\n",
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" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
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" 3/3 0.759G 0.925 2.507 1.254 17 640: 100% 1/1 [00:00<00:00, 7.53it/s]\n",
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" Class Images Instances Box(P R mAP50 mAP50-95): 100% 1/1 [00:00<00:00, 6.80it/s]\n",
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" all 4 17 0.906 0.532 0.741 0.513\n",
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" 3/3 0.776G 0.6701 3.697 1.096 17 640: 100% 1/1 [00:00<00:00, 6.45it/s]\n",
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" Class Images Instances Box(P R mAP50 mAP50-95): 100% 1/1 [00:00<00:00, 5.66it/s]\n",
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" all 4 17 0.577 0.833 0.874 0.614\n",
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"\n",
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"3 epochs completed in 0.002 hours.\n",
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"Optimizer stripped from runs/detect/train/weights/last.pt, 6.5MB\n",
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"Optimizer stripped from runs/detect/train/weights/best.pt, 6.5MB\n",
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"\n",
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"Validating runs/detect/train/weights/best.pt...\n",
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"Ultralytics YOLOv8.1.23 🚀 Python-3.10.12 torch-2.1.0+cu121 CUDA:0 (Tesla T4, 15102MiB)\n",
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"Ultralytics YOLOv8.2.3 🚀 Python-3.10.12 torch-2.2.1+cu121 CUDA:0 (Tesla T4, 15102MiB)\n",
|
||||
"Model summary (fused): 168 layers, 3151904 parameters, 0 gradients, 8.7 GFLOPs\n",
|
||||
" Class Images Instances Box(P R mAP50 mAP50-95): 100% 1/1 [00:00<00:00, 16.31it/s]\n",
|
||||
" all 4 17 0.906 0.533 0.755 0.515\n",
|
||||
" person 4 10 0.942 0.3 0.519 0.233\n",
|
||||
" dog 4 1 1 0 0.332 0.162\n",
|
||||
" horse 4 2 1 0.9 0.995 0.698\n",
|
||||
" elephant 4 2 1 0 0.695 0.206\n",
|
||||
" umbrella 4 1 0.755 1 0.995 0.895\n",
|
||||
" potted plant 4 1 0.739 1 0.995 0.895\n",
|
||||
"Speed: 0.3ms preprocess, 6.1ms inference, 0.0ms loss, 2.5ms postprocess per image\n",
|
||||
" Class Images Instances Box(P R mAP50 mAP50-95): 100% 1/1 [00:00<00:00, 18.23it/s]\n",
|
||||
" all 4 17 0.617 0.884 0.888 0.622\n",
|
||||
" person 4 10 0.67 0.5 0.52 0.278\n",
|
||||
" dog 4 1 0.361 1 0.995 0.597\n",
|
||||
" horse 4 2 0.728 1 0.995 0.631\n",
|
||||
" elephant 4 2 0.602 0.805 0.828 0.332\n",
|
||||
" umbrella 4 1 0.553 1 0.995 0.995\n",
|
||||
" potted plant 4 1 0.789 1 0.995 0.895\n",
|
||||
"Speed: 0.3ms preprocess, 4.1ms inference, 0.0ms loss, 1.2ms postprocess per image\n",
|
||||
"Results saved to \u001b[1mruns/detect/train\u001b[0m\n",
|
||||
"💡 Learn more at https://docs.ultralytics.com/modes/train\n"
|
||||
]
|
||||
|
|
@ -348,21 +355,21 @@
|
|||
"- 💡 ProTip: Export to [ONNX](https://docs.ultralytics.com/integrations/onnx/) or [OpenVINO](https://docs.ultralytics.com/integrations/openvino/) for up to 3x CPU speedup. \n",
|
||||
"- 💡 ProTip: Export to [TensorRT](https://docs.ultralytics.com/integrations/tensorrt/) for up to 5x GPU speedup.\n",
|
||||
"\n",
|
||||
"| Format | `format` Argument | Model | Metadata | Arguments |\n",
|
||||
"|--------------------------------------------------------------------|-------------------|---------------------------|----------|-----------------------------------------------------|\n",
|
||||
"| [PyTorch](https://pytorch.org/) | - | `yolov8n.pt` | ✅ | - |\n",
|
||||
"| [TorchScript](https://pytorch.org/docs/stable/jit.html) | `torchscript` | `yolov8n.torchscript` | ✅ | `imgsz`, `optimize` |\n",
|
||||
"| [ONNX](https://onnx.ai/) | `onnx` | `yolov8n.onnx` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `opset` |\n",
|
||||
"| [OpenVINO](https://docs.openvino.ai/) | `openvino` | `yolov8n_openvino_model/` | ✅ | `imgsz`, `half`, `int8` |\n",
|
||||
"| [TensorRT](https://developer.nvidia.com/tensorrt) | `engine` | `yolov8n.engine` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `workspace` |\n",
|
||||
"| [CoreML](https://github.com/apple/coremltools) | `coreml` | `yolov8n.mlpackage` | ✅ | `imgsz`, `half`, `int8`, `nms` |\n",
|
||||
"| [TF SavedModel](https://www.tensorflow.org/guide/saved_model) | `saved_model` | `yolov8n_saved_model/` | ✅ | `imgsz`, `keras`, `int8` |\n",
|
||||
"| [TF GraphDef](https://www.tensorflow.org/api_docs/python/tf/Graph) | `pb` | `yolov8n.pb` | ❌ | `imgsz` |\n",
|
||||
"| [TF Lite](https://www.tensorflow.org/lite) | `tflite` | `yolov8n.tflite` | ✅ | `imgsz`, `half`, `int8` |\n",
|
||||
"| [TF Edge TPU](https://coral.ai/docs/edgetpu/models-intro/) | `edgetpu` | `yolov8n_edgetpu.tflite` | ✅ | `imgsz` |\n",
|
||||
"| [TF.js](https://www.tensorflow.org/js) | `tfjs` | `yolov8n_web_model/` | ✅ | `imgsz`, `half`, `int8` |\n",
|
||||
"| [PaddlePaddle](https://github.com/PaddlePaddle) | `paddle` | `yolov8n_paddle_model/` | ✅ | `imgsz` |\n",
|
||||
"| [NCNN](https://github.com/Tencent/ncnn) | `ncnn` | `yolov8n_ncnn_model/` | ✅ | `imgsz`, `half` |\n"
|
||||
"| Format | `format` Argument | Model | Metadata | Arguments |\n",
|
||||
"|--------------------------------------------------------------------------|-------------------|---------------------------|----------|--------------------------------------------------------------|\n",
|
||||
"| [PyTorch](https://pytorch.org/) | - | `yolov8n.pt` | ✅ | - |\n",
|
||||
"| [TorchScript](https://docs.ultralytics.com/integrations/torchscript) | `torchscript` | `yolov8n.torchscript` | ✅ | `imgsz`, `optimize`, `batch` |\n",
|
||||
"| [ONNX](https://docs.ultralytics.com/integrations/onnx) | `onnx` | `yolov8n.onnx` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `opset`, `batch` |\n",
|
||||
"| [OpenVINO](https://docs.ultralytics.com/integrations/openvino) | `openvino` | `yolov8n_openvino_model/` | ✅ | `imgsz`, `half`, `int8`, `batch` |\n",
|
||||
"| [TensorRT](https://docs.ultralytics.com/integrations/tensorrt) | `engine` | `yolov8n.engine` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `workspace`, `batch` |\n",
|
||||
"| [CoreML](https://docs.ultralytics.com/integrations/coreml) | `coreml` | `yolov8n.mlpackage` | ✅ | `imgsz`, `half`, `int8`, `nms`, `batch` |\n",
|
||||
"| [TF SavedModel](https://docs.ultralytics.com/integrations/tf-savedmodel) | `saved_model` | `yolov8n_saved_model/` | ✅ | `imgsz`, `keras`, `int8`, `batch` |\n",
|
||||
"| [TF GraphDef](https://docs.ultralytics.com/integrations/tf-graphdef) | `pb` | `yolov8n.pb` | ❌ | `imgsz`, `batch` |\n",
|
||||
"| [TF Lite](https://docs.ultralytics.com/integrations/tflite) | `tflite` | `yolov8n.tflite` | ✅ | `imgsz`, `half`, `int8`, `batch` |\n",
|
||||
"| [TF Edge TPU](https://docs.ultralytics.com/integrations/edge-tpu) | `edgetpu` | `yolov8n_edgetpu.tflite` | ✅ | `imgsz`, `batch` |\n",
|
||||
"| [TF.js](https://docs.ultralytics.com/integrations/tfjs) | `tfjs` | `yolov8n_web_model/` | ✅ | `imgsz`, `half`, `int8`, `batch` |\n",
|
||||
"| [PaddlePaddle](https://docs.ultralytics.com/integrations/paddlepaddle) | `paddle` | `yolov8n_paddle_model/` | ✅ | `imgsz`, `batch` |\n",
|
||||
"| [NCNN](https://docs.ultralytics.com/integrations/ncnn) | `ncnn` | `yolov8n_ncnn_model/` | ✅ | `imgsz`, `half`, `batch` |"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "nPZZeNrLCQG6"
|
||||
|
|
@ -378,7 +385,7 @@
|
|||
"base_uri": "https://localhost:8080/"
|
||||
},
|
||||
"id": "CYIjW4igCjqD",
|
||||
"outputId": "f6d45666-07b4-4214-86c0-4e83e70ac096"
|
||||
"outputId": "947e65cc-79c8-4713-bfd4-3139903ac05a"
|
||||
},
|
||||
"execution_count": 5,
|
||||
"outputs": [
|
||||
|
|
@ -386,15 +393,15 @@
|
|||
"output_type": "stream",
|
||||
"name": "stdout",
|
||||
"text": [
|
||||
"Ultralytics YOLOv8.1.23 🚀 Python-3.10.12 torch-2.1.0+cu121 CPU (Intel Xeon 2.30GHz)\n",
|
||||
"Ultralytics YOLOv8.2.3 🚀 Python-3.10.12 torch-2.2.1+cu121 CPU (Intel Xeon 2.00GHz)\n",
|
||||
"YOLOv8n summary (fused): 168 layers, 3151904 parameters, 0 gradients, 8.7 GFLOPs\n",
|
||||
"\n",
|
||||
"\u001b[34m\u001b[1mPyTorch:\u001b[0m starting from 'yolov8n.pt' with input shape (1, 3, 640, 640) BCHW and output shape(s) (1, 84, 8400) (6.2 MB)\n",
|
||||
"\n",
|
||||
"\u001b[34m\u001b[1mTorchScript:\u001b[0m starting export with torch 2.1.0+cu121...\n",
|
||||
"\u001b[34m\u001b[1mTorchScript:\u001b[0m export success ✅ 2.4s, saved as 'yolov8n.torchscript' (12.4 MB)\n",
|
||||
"\u001b[34m\u001b[1mTorchScript:\u001b[0m starting export with torch 2.2.1+cu121...\n",
|
||||
"\u001b[34m\u001b[1mTorchScript:\u001b[0m export success ✅ 2.0s, saved as 'yolov8n.torchscript' (12.4 MB)\n",
|
||||
"\n",
|
||||
"Export complete (4.5s)\n",
|
||||
"Export complete (4.0s)\n",
|
||||
"Results saved to \u001b[1m/content\u001b[0m\n",
|
||||
"Predict: yolo predict task=detect model=yolov8n.torchscript imgsz=640 \n",
|
||||
"Validate: yolo val task=detect model=yolov8n.torchscript imgsz=640 data=coco.yaml \n",
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue