Update YOLO11 notebooks (#16608)
Signed-off-by: UltralyticsAssistant <web@ultralytics.com> Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
This commit is contained in:
Glenn Jocher
GitHub
parent
29ffc6e0db
commit
a5265d91d2
5 changed files with 92 additions and 92 deletions
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"nbformat_minor": 0,
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"metadata": {
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"colab": {
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"name": "YOLOv8 Tutorial",
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"name": "YOLO11 Tutorial",
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"provenance": [],
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"toc_visible": true
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},
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@ -36,11 +36,11 @@
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" <a href=\"https://community.ultralytics.com\"><img alt=\"Ultralytics Forums\" src=\"https://img.shields.io/discourse/users?server=https%3A%2F%2Fcommunity.ultralytics.com&logo=discourse&label=Forums&color=blue\"></a>\n",
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" <a href=\"https://reddit.com/r/ultralytics\"><img alt=\"Ultralytics Reddit\" src=\"https://img.shields.io/reddit/subreddit-subscribers/ultralytics?style=flat&logo=reddit&logoColor=white&label=Reddit&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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"Welcome to the Ultralytics YOLO11 🚀 notebook! <a href=\"https://github.com/ultralytics/ultralytics\">YOLO11</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 YOLO11 and understand its features and capabilities.\n",
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"\n",
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"YOLOv8 models are fast, accurate, and easy to use, making them ideal for various object detection and image segmentation tasks. They can be trained on large datasets and run on diverse hardware platforms, from CPUs to GPUs.\n",
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"YOLO11 models are fast, accurate, and easy to use, making them ideal for various object detection and image segmentation tasks. They can be trained on large datasets and run on diverse hardware platforms, from CPUs to GPUs.\n",
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"\n",
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"We hope that the resources in this notebook will help you get the most out of YOLOv8. Please browse the YOLOv8 <a href=\"https://docs.ultralytics.com/\">Docs</a> for details, raise an issue on <a href=\"https://github.com/ultralytics/ultralytics\">GitHub</a> for support, and join our <a href=\"https://ultralytics.com/discord\">Discord</a> community for questions and discussions!\n",
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"We hope that the resources in this notebook will help you get the most out of YOLO11. Please browse the YOLO11 <a href=\"https://docs.ultralytics.com/\">Docs</a> for details, raise an issue on <a href=\"https://github.com/ultralytics/ultralytics\">GitHub</a> for support, and join our <a href=\"https://ultralytics.com/discord\">Discord</a> community for questions and discussions!\n",
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"\n",
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"</div>"
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]
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@ -78,7 +78,7 @@
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"Ultralytics YOLOv8.2.3 🚀 Python-3.10.12 torch-2.2.1+cu121 CUDA:0 (T4, 15102MiB)\n",
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"Ultralytics 8.3.0 🚀 Python-3.10.12 torch-2.2.1+cu121 CUDA:0 (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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@ -92,7 +92,7 @@
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"source": [
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"# 1. Predict\n",
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"\n",
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"YOLOv8 may be used directly in the Command Line Interface (CLI) with a `yolo` command for a variety of tasks and modes and accepts additional arguments, i.e. `imgsz=640`. See a full list of available `yolo` [arguments](https://docs.ultralytics.com/usage/cfg/) and other details in the [YOLOv8 Predict Docs](https://docs.ultralytics.com/modes/train/).\n"
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"YOLO11 may be used directly in the Command Line Interface (CLI) with a `yolo` command for a variety of tasks and modes and accepts additional arguments, i.e. `imgsz=640`. See a full list of available `yolo` [arguments](https://docs.ultralytics.com/usage/cfg/) and other details in the [YOLO11 Predict Docs](https://docs.ultralytics.com/modes/train/).\n"
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]
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},
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{
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@ -106,7 +106,7 @@
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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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"!yolo predict model=yolov8n.pt source='https://ultralytics.com/images/zidane.jpg'"
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"!yolo predict model=yolo11n.pt source='https://ultralytics.com/images/zidane.jpg'"
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],
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"execution_count": null,
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"outputs": [
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@ -116,7 +116,7 @@
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"text": [
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"Downloading https://github.com/ultralytics/assets/releases/download/v8.3.0/yolov8n.pt to 'yolov8n.pt'...\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 (T4, 15102MiB)\n",
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"Ultralytics 8.3.0 🚀 Python-3.10.12 torch-2.2.1+cu121 CUDA:0 (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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@ -146,7 +146,7 @@
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},
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"source": [
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"# 2. Val\n",
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"Validate a model's accuracy on the [COCO](https://docs.ultralytics.com/datasets/detect/coco/) dataset's `val` or `test` splits. The latest YOLOv8 [models](https://github.com/ultralytics/ultralytics#models) are downloaded automatically the first time they are used. See [YOLOv8 Val Docs](https://docs.ultralytics.com/modes/val/) for more information."
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"Validate a model's accuracy on the [COCO](https://docs.ultralytics.com/datasets/detect/coco/) dataset's `val` or `test` splits. The latest YOLO11 [models](https://github.com/ultralytics/ultralytics#models) are downloaded automatically the first time they are used. See [YOLO11 Val Docs](https://docs.ultralytics.com/modes/val/) for more information."
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]
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},
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{
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@ -174,7 +174,7 @@
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},
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"source": [
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"# Validate YOLOv8n on COCO8 val\n",
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"!yolo val model=yolov8n.pt data=coco8.yaml"
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"!yolo val model=yolo11n.pt data=coco8.yaml"
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],
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"execution_count": null,
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"outputs": [
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@ -182,7 +182,7 @@
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"Ultralytics YOLOv8.2.3 🚀 Python-3.10.12 torch-2.2.1+cu121 CUDA:0 (T4, 15102MiB)\n",
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"Ultralytics 8.3.0 🚀 Python-3.10.12 torch-2.2.1+cu121 CUDA:0 (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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@ -220,13 +220,13 @@
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"\n",
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"<p align=\"\"><a href=\"https://ultralytics.com/hub\"><img width=\"1000\" src=\"https://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png\"/></a></p>\n",
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"\n",
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"Train YOLOv8 on [Detect](https://docs.ultralytics.com/tasks/detect/), [Segment](https://docs.ultralytics.com/tasks/segment/), [Classify](https://docs.ultralytics.com/tasks/classify/) and [Pose](https://docs.ultralytics.com/tasks/pose/) datasets. See [YOLOv8 Train Docs](https://docs.ultralytics.com/modes/train/) for more information."
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"Train YOLO11 on [Detect](https://docs.ultralytics.com/tasks/detect/), [Segment](https://docs.ultralytics.com/tasks/segment/), [Classify](https://docs.ultralytics.com/tasks/classify/) and [Pose](https://docs.ultralytics.com/tasks/pose/) datasets. See [YOLO11 Train Docs](https://docs.ultralytics.com/modes/train/) for more information."
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]
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},
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{
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"cell_type": "code",
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"source": [
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"#@title Select YOLOv8 🚀 logger {run: 'auto'}\n",
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"#@title Select YOLO11 🚀 logger {run: 'auto'}\n",
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"logger = 'Comet' #@param ['Comet', 'TensorBoard']\n",
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"\n",
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"if logger == 'Comet':\n",
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@ -253,7 +253,7 @@
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},
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"source": [
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"# Train YOLOv8n on COCO8 for 3 epochs\n",
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"!yolo train model=yolov8n.pt data=coco8.yaml epochs=3 imgsz=640"
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"!yolo train model=yolo11n.pt data=coco8.yaml epochs=3 imgsz=640"
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],
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"execution_count": null,
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"outputs": [
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@ -261,8 +261,8 @@
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"Ultralytics YOLOv8.2.3 🚀 Python-3.10.12 torch-2.2.1+cu121 CUDA:0 (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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"Ultralytics 8.3.0 🚀 Python-3.10.12 torch-2.2.1+cu121 CUDA:0 (T4, 15102MiB)\n",
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"\u001b[34m\u001b[1mengine/trainer: \u001b[0mtask=detect, mode=train, model=yolo11n.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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"\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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@ -330,7 +330,7 @@
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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 YOLOv8.2.3 🚀 Python-3.10.12 torch-2.2.1+cu121 CUDA:0 (T4, 15102MiB)\n",
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"Ultralytics 8.3.0 🚀 Python-3.10.12 torch-2.2.1+cu121 CUDA:0 (T4, 15102MiB)\n",
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"Model summary (fused): 168 layers, 3151904 parameters, 0 gradients, 8.7 GFLOPs\n",
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" Class Images Instances Box(P R mAP50 mAP50-95): 100% 1/1 [00:00<00:00, 18.23it/s]\n",
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" all 4 17 0.617 0.884 0.888 0.622\n",
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@ -352,26 +352,26 @@
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"source": [
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"# 4. Export\n",
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"\n",
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"Export a YOLOv8 model to any supported format below with the `format` argument, i.e. `format=onnx`. See [YOLOv8 Export Docs](https://docs.ultralytics.com/modes/export/) for more information.\n",
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"Export a YOLO11 model to any supported format below with the `format` argument, i.e. `format=onnx`. See [YOLO11 Export Docs](https://docs.ultralytics.com/modes/export/) for more information.\n",
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"\n",
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"- 💡 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",
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"- 💡 ProTip: Export to [TensorRT](https://docs.ultralytics.com/integrations/tensorrt/) for up to 5x GPU speedup.\n",
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"\n",
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"| Format | `format` Argument | Model | Metadata | Arguments |\n",
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"|--------------------------------------------------------------------------|-------------------|---------------------------|----------|----------------------------------------------------------------------|\n",
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"| [PyTorch](https://pytorch.org/) | - | `yolov8n.pt` | ✅ | - |\n",
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"| [TorchScript](https://docs.ultralytics.com/integrations/torchscript) | `torchscript` | `yolov8n.torchscript` | ✅ | `imgsz`, `optimize`, `batch` |\n",
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"| [ONNX](https://docs.ultralytics.com/integrations/onnx) | `onnx` | `yolov8n.onnx` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `opset`, `batch` |\n",
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"| [OpenVINO](https://docs.ultralytics.com/integrations/openvino) | `openvino` | `yolov8n_openvino_model/` | ✅ | `imgsz`, `half`, `int8`, `batch` |\n",
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"| [TensorRT](https://docs.ultralytics.com/integrations/tensorrt) | `engine` | `yolov8n.engine` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `workspace`, `int8`, `batch` |\n",
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"| [CoreML](https://docs.ultralytics.com/integrations/coreml) | `coreml` | `yolov8n.mlpackage` | ✅ | `imgsz`, `half`, `int8`, `nms`, `batch` |\n",
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"| [TF SavedModel](https://docs.ultralytics.com/integrations/tf-savedmodel) | `saved_model` | `yolov8n_saved_model/` | ✅ | `imgsz`, `keras`, `int8`, `batch` |\n",
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"| [TF GraphDef](https://docs.ultralytics.com/integrations/tf-graphdef) | `pb` | `yolov8n.pb` | ❌ | `imgsz`, `batch` |\n",
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"| [TF Lite](https://docs.ultralytics.com/integrations/tflite) | `tflite` | `yolov8n.tflite` | ✅ | `imgsz`, `half`, `int8`, `batch` |\n",
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"| [TF Edge TPU](https://docs.ultralytics.com/integrations/edge-tpu) | `edgetpu` | `yolov8n_edgetpu.tflite` | ✅ | `imgsz`, `batch` |\n",
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"| [TF.js](https://docs.ultralytics.com/integrations/tfjs) | `tfjs` | `yolov8n_web_model/` | ✅ | `imgsz`, `half`, `int8`, `batch` |\n",
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"| [PaddlePaddle](https://docs.ultralytics.com/integrations/paddlepaddle) | `paddle` | `yolov8n_paddle_model/` | ✅ | `imgsz`, `batch` |\n",
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"| [NCNN](https://docs.ultralytics.com/integrations/ncnn) | `ncnn` | `yolov8n_ncnn_model/` | ✅ | `imgsz`, `half`, `batch` |"
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"| [PyTorch](https://pytorch.org/) | - | `yolo11n.pt` | ✅ | - |\n",
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"| [TorchScript](https://docs.ultralytics.com/integrations/torchscript) | `torchscript` | `yolo11n.torchscript` | ✅ | `imgsz`, `optimize`, `batch` |\n",
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"| [ONNX](https://docs.ultralytics.com/integrations/onnx) | `onnx` | `yolo11n.onnx` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `opset`, `batch` |\n",
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"| [OpenVINO](https://docs.ultralytics.com/integrations/openvino) | `openvino` | `yolo11n_openvino_model/` | ✅ | `imgsz`, `half`, `int8`, `batch` |\n",
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"| [TensorRT](https://docs.ultralytics.com/integrations/tensorrt) | `engine` | `yolo11n.engine` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `workspace`, `int8`, `batch` |\n",
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"| [CoreML](https://docs.ultralytics.com/integrations/coreml) | `coreml` | `yolo11n.mlpackage` | ✅ | `imgsz`, `half`, `int8`, `nms`, `batch` |\n",
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"| [TF SavedModel](https://docs.ultralytics.com/integrations/tf-savedmodel) | `saved_model` | `yolo11n_saved_model/` | ✅ | `imgsz`, `keras`, `int8`, `batch` |\n",
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"| [TF GraphDef](https://docs.ultralytics.com/integrations/tf-graphdef) | `pb` | `yolo11n.pb` | ❌ | `imgsz`, `batch` |\n",
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"| [TF Lite](https://docs.ultralytics.com/integrations/tflite) | `tflite` | `yolo11n.tflite` | ✅ | `imgsz`, `half`, `int8`, `batch` |\n",
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"| [TF Edge TPU](https://docs.ultralytics.com/integrations/edge-tpu) | `edgetpu` | `yolo11n_edgetpu.tflite` | ✅ | `imgsz` |\n",
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"| [TF.js](https://docs.ultralytics.com/integrations/tfjs) | `tfjs` | `yolo11n_web_model/` | ✅ | `imgsz`, `half`, `int8`, `batch` |\n",
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"| [PaddlePaddle](https://docs.ultralytics.com/integrations/paddlepaddle) | `paddle` | `yolo11n_paddle_model/` | ✅ | `imgsz`, `batch` |\n",
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"| [NCNN](https://docs.ultralytics.com/integrations/ncnn) | `ncnn` | `yolo11n_ncnn_model/` | ✅ | `imgsz`, `half`, `batch` |"
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],
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"metadata": {
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"id": "nPZZeNrLCQG6"
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{
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"cell_type": "code",
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"source": [
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"!yolo export model=yolov8n.pt format=torchscript"
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"!yolo export model=yolo11n.pt format=torchscript"
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],
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"metadata": {
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"colab": {
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"Ultralytics YOLOv8.2.3 🚀 Python-3.10.12 torch-2.2.1+cu121 CPU (Intel Xeon 2.00GHz)\n",
|
||||
"Ultralytics 8.3.0 🚀 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",
|
||||
"\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) (6.2 MB)\n",
|
||||
"\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",
|
||||
"\u001b[34m\u001b[1mTorchScript:\u001b[0m export success ✅ 2.0s, saved as 'yolo11n.torchscript' (12.4 MB)\n",
|
||||
"\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",
|
||||
"Predict: yolo predict task=detect model=yolo11n.torchscript imgsz=640 \n",
|
||||
"Validate: yolo val task=detect model=yolo11n.torchscript imgsz=640 data=coco.yaml \n",
|
||||
"Visualize: https://netron.app\n",
|
||||
"💡 Learn more at https://docs.ultralytics.com/modes/export\n"
|
||||
]
|
||||
|
|
@ -418,7 +418,7 @@
|
|||
"source": [
|
||||
"# 5. Python Usage\n",
|
||||
"\n",
|
||||
"YOLOv8 was reimagined using Python-first principles for the most seamless Python YOLO experience yet. YOLOv8 models can be loaded from a trained checkpoint or created from scratch. Then methods are used to train, val, predict, and export the model. See detailed Python usage examples in the [YOLOv8 Python Docs](https://docs.ultralytics.com/usage/python/)."
|
||||
"YOLO11 was reimagined using Python-first principles for the most seamless Python YOLO experience yet. YOLO11 models can be loaded from a trained checkpoint or created from scratch. Then methods are used to train, val, predict, and export the model. See detailed Python usage examples in the [YOLO11 Python Docs](https://docs.ultralytics.com/usage/python/)."
|
||||
],
|
||||
"metadata": {
|
||||
"id": "kUMOQ0OeDBJG"
|
||||
|
|
@ -430,8 +430,8 @@
|
|||
"from ultralytics import YOLO\n",
|
||||
"\n",
|
||||
"# Load a model\n",
|
||||
"model = YOLO('yolov8n.yaml') # build a new model from scratch\n",
|
||||
"model = YOLO('yolov8n.pt') # load a pretrained model (recommended for training)\n",
|
||||
"model = YOLO('yolo11n.yaml') # build a new model from scratch\n",
|
||||
"model = YOLO('yolo11n.pt') # load a pretrained model (recommended for training)\n",
|
||||
"\n",
|
||||
"# Use the model\n",
|
||||
"results = model.train(data='coco8.yaml', epochs=3) # train the model\n",
|
||||
|
|
@ -450,7 +450,7 @@
|
|||
"source": [
|
||||
"# 6. Tasks\n",
|
||||
"\n",
|
||||
"YOLOv8 can train, val, predict and export models for the most common tasks in vision AI: [Detect](https://docs.ultralytics.com/tasks/detect/), [Segment](https://docs.ultralytics.com/tasks/segment/), [Classify](https://docs.ultralytics.com/tasks/classify/) and [Pose](https://docs.ultralytics.com/tasks/pose/). See [YOLOv8 Tasks Docs](https://docs.ultralytics.com/tasks/) for more information.\n",
|
||||
"YOLO11 can train, val, predict and export models for the most common tasks in vision AI: [Detect](https://docs.ultralytics.com/tasks/detect/), [Segment](https://docs.ultralytics.com/tasks/segment/), [Classify](https://docs.ultralytics.com/tasks/classify/) and [Pose](https://docs.ultralytics.com/tasks/pose/). See [YOLO11 Tasks Docs](https://docs.ultralytics.com/tasks/) for more information.\n",
|
||||
"\n",
|
||||
"<br><img width=\"1024\" src=\"https://raw.githubusercontent.com/ultralytics/assets/main/im/banner-tasks.png\">\n"
|
||||
],
|
||||
|
|
@ -463,7 +463,7 @@
|
|||
"source": [
|
||||
"## 1. Detection\n",
|
||||
"\n",
|
||||
"YOLOv8 _detection_ models have no suffix and are the default YOLOv8 models, i.e. `yolov8n.pt` and are pretrained on COCO. See [Detection Docs](https://docs.ultralytics.com/tasks/detect/) for full details.\n"
|
||||
"YOLO11 _detection_ models have no suffix and are the default YOLO11 models, i.e. `yolo11n.pt` and are pretrained on COCO. See [Detection Docs](https://docs.ultralytics.com/tasks/detect/) for full details.\n"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "yq26lwpYK1lq"
|
||||
|
|
@ -475,7 +475,7 @@
|
|||
"# Load YOLOv8n, train it on COCO128 for 3 epochs and predict an image with it\n",
|
||||
"from ultralytics import YOLO\n",
|
||||
"\n",
|
||||
"model = YOLO('yolov8n.pt') # load a pretrained YOLOv8n detection model\n",
|
||||
"model = YOLO('yolo11n.pt') # load a pretrained YOLOv8n detection model\n",
|
||||
"model.train(data='coco8.yaml', epochs=3) # train the model\n",
|
||||
"model('https://ultralytics.com/images/bus.jpg') # predict on an image"
|
||||
],
|
||||
|
|
@ -490,7 +490,7 @@
|
|||
"source": [
|
||||
"## 2. Segmentation\n",
|
||||
"\n",
|
||||
"YOLOv8 _segmentation_ models use the `-seg` suffix, i.e. `yolov8n-seg.pt` and are pretrained on COCO. See [Segmentation Docs](https://docs.ultralytics.com/tasks/segment/) for full details.\n"
|
||||
"YOLO11 _segmentation_ models use the `-seg` suffix, i.e. `yolo11n-seg.pt` and are pretrained on COCO. See [Segmentation Docs](https://docs.ultralytics.com/tasks/segment/) for full details.\n"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "7ZW58jUzK66B"
|
||||
|
|
@ -502,7 +502,7 @@
|
|||
"# Load YOLOv8n-seg, train it on COCO128-seg for 3 epochs and predict an image with it\n",
|
||||
"from ultralytics import YOLO\n",
|
||||
"\n",
|
||||
"model = YOLO('yolov8n-seg.pt') # load a pretrained YOLOv8n segmentation model\n",
|
||||
"model = YOLO('yolo11n-seg.pt') # load a pretrained YOLOv8n segmentation model\n",
|
||||
"model.train(data='coco8-seg.yaml', epochs=3) # train the model\n",
|
||||
"model('https://ultralytics.com/images/bus.jpg') # predict on an image"
|
||||
],
|
||||
|
|
@ -517,7 +517,7 @@
|
|||
"source": [
|
||||
"## 3. Classification\n",
|
||||
"\n",
|
||||
"YOLOv8 _classification_ models use the `-cls` suffix, i.e. `yolov8n-cls.pt` and are pretrained on ImageNet. See [Classification Docs](https://docs.ultralytics.com/tasks/classify/) for full details.\n"
|
||||
"YOLO11 _classification_ models use the `-cls` suffix, i.e. `yolo11n-cls.pt` and are pretrained on ImageNet. See [Classification Docs](https://docs.ultralytics.com/tasks/classify/) for full details.\n"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "ax3p94VNK9zR"
|
||||
|
|
@ -529,7 +529,7 @@
|
|||
"# Load YOLOv8n-cls, train it on mnist160 for 3 epochs and predict an image with it\n",
|
||||
"from ultralytics import YOLO\n",
|
||||
"\n",
|
||||
"model = YOLO('yolov8n-cls.pt') # load a pretrained YOLOv8n classification model\n",
|
||||
"model = YOLO('yolo11n-cls.pt') # load a pretrained YOLOv8n classification model\n",
|
||||
"model.train(data='mnist160', epochs=3) # train the model\n",
|
||||
"model('https://ultralytics.com/images/bus.jpg') # predict on an image"
|
||||
],
|
||||
|
|
@ -544,7 +544,7 @@
|
|||
"source": [
|
||||
"## 4. Pose\n",
|
||||
"\n",
|
||||
"YOLOv8 _pose_ models use the `-pose` suffix, i.e. `yolov8n-pose.pt` and are pretrained on COCO Keypoints. See [Pose Docs](https://docs.ultralytics.com/tasks/pose/) for full details."
|
||||
"YOLO11 _pose_ models use the `-pose` suffix, i.e. `yolo11n-pose.pt` and are pretrained on COCO Keypoints. See [Pose Docs](https://docs.ultralytics.com/tasks/pose/) for full details."
|
||||
],
|
||||
"metadata": {
|
||||
"id": "SpIaFLiO11TG"
|
||||
|
|
@ -556,7 +556,7 @@
|
|||
"# Load YOLOv8n-pose, train it on COCO8-pose for 3 epochs and predict an image with it\n",
|
||||
"from ultralytics import YOLO\n",
|
||||
"\n",
|
||||
"model = YOLO('yolov8n-pose.pt') # load a pretrained YOLOv8n pose model\n",
|
||||
"model = YOLO('yolo11n-pose.pt') # load a pretrained YOLOv8n pose model\n",
|
||||
"model.train(data='coco8-pose.yaml', epochs=3) # train the model\n",
|
||||
"model('https://ultralytics.com/images/bus.jpg') # predict on an image"
|
||||
],
|
||||
|
|
@ -571,7 +571,7 @@
|
|||
"source": [
|
||||
"## 4. Oriented Bounding Boxes (OBB)\n",
|
||||
"\n",
|
||||
"YOLOv8 _OBB_ models use the `-obb` suffix, i.e. `yolov8n-obb.pt` and are pretrained on the DOTA dataset. See [OBB Docs](https://docs.ultralytics.com/tasks/obb/) for full details."
|
||||
"YOLO11 _OBB_ models use the `-obb` suffix, i.e. `yolo11n-obb.pt` and are pretrained on the DOTA dataset. See [OBB Docs](https://docs.ultralytics.com/tasks/obb/) for full details."
|
||||
],
|
||||
"metadata": {
|
||||
"id": "cf5j_T9-B5F0"
|
||||
|
|
@ -583,7 +583,7 @@
|
|||
"# Load YOLOv8n-obb, train it on DOTA8 for 3 epochs and predict an image with it\n",
|
||||
"from ultralytics import YOLO\n",
|
||||
"\n",
|
||||
"model = YOLO('yolov8n-obb.pt') # load a pretrained YOLOv8n OBB model\n",
|
||||
"model = YOLO('yolo11n-obb.pt') # load a pretrained YOLOv8n OBB model\n",
|
||||
"model.train(data='coco8-dota.yaml', epochs=3) # train the model\n",
|
||||
"model('https://ultralytics.com/images/bus.jpg') # predict on an image"
|
||||
],
|
||||
|
|
@ -646,7 +646,7 @@
|
|||
"source": [
|
||||
"# Validate multiple models\n",
|
||||
"for x in 'nsmlx':\n",
|
||||
" !yolo val model=yolov8{x}.pt data=coco.yaml"
|
||||
" !yolo val model=yolo11{x}.pt data=coco.yaml"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "Wdc6t_bfzDDk"
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue