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@@ -14,7 +14,7 @@ language:
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  - ar
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  library_name: ultralytics
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  model-index:
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- - name: ultralytics/yolo26
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  results:
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  - task:
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  type: object-detection
@@ -30,7 +30,7 @@ model-index:
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  <div align="center">
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  <p>
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- <a href="https://www.ultralytics.com/blog/ultralytics-yolo26-the-new-standard-for-edge-first-vision-ai" target="_blank">
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  <img width="100%" src="https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/banner-yolov8.png" alt="Ultralytics YOLO banner"></a>
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  </p>
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@@ -47,21 +47,19 @@ model-index:
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  <a href="https://www.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>
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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 Ultralytics on Gradient"></a>
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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 Ultralytics In Colab"></a>
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- <a href="https://www.kaggle.com/models/ultralytics/yolo26" style="position: relative; top: -3px;"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open Ultralytics In Kaggle"></a>
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  <a href="https://mybinder.org/v2/gh/ultralytics/ultralytics/HEAD?labpath=examples%2Ftutorial.ipynb" style="position: relative; top: -3px;"><img src="https://mybinder.org/badge_logo.svg" alt="Open Ultralytics In Binder"></a>
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  </div>
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  </div>
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  <br>
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- [Ultralytics](https://www.ultralytics.com/) [YOLO26](https://github.com/ultralytics/ultralytics) is a cutting-edge, state-of-the-art (SOTA) model that builds upon the success of previous YOLO versions and introduces new features and improvements to further boost performance and flexibility. YOLO26 is designed to be fast, accurate, and easy to use, making it an excellent choice for a wide range of object detection and tracking, instance segmentation, image classification and pose estimation tasks.
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  We hope that the resources here will help you get the most out of YOLO. Please browse the Ultralytics <a href="https://docs.ultralytics.com/">Docs</a> for details, raise an issue on <a href="https://github.com/ultralytics/ultralytics/issues/new/choose">GitHub</a> for support, questions, or discussions, become a member of the Ultralytics <a href="https://discord.com/invite/ultralytics">Discord</a>, <a href="https://reddit.com/r/ultralytics">Reddit</a> and <a href="https://community.ultralytics.com/">Forums</a>!
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  To request an Enterprise License please complete the form at [Ultralytics Licensing](https://www.ultralytics.com/license).
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- <a href="https://platform.ultralytics.com/ultralytics/yolo26" target="_blank">
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- <img width="100%" src="https://raw.githubusercontent.com/ultralytics/assets/refs/heads/main/yolo/performance-comparison.png" alt="YOLO26 performance plots">
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- </a>
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  <div style="display: flex; justify-content: center; flex-wrap: wrap;">
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  <a href="https://github.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-github.png" width="35%" alt="Ultralytics GitHub"></a>
@@ -103,7 +101,7 @@ For alternative installation methods including [Conda](https://anaconda.org/cond
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  YOLO may be used directly in the Command Line Interface (CLI) with a `yolo` command:
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  ```bash
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- yolo predict model=yolo26n.pt source='https://ultralytics.com/images/bus.jpg'
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  ```
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  `yolo` can be used for a variety of tasks and modes and accepts additional arguments, i.e. `imgsz=640`. See the YOLO [CLI Docs](https://docs.ultralytics.com/usage/cli/) for examples.
@@ -116,7 +114,7 @@ YOLO may also be used directly in a Python environment, and accepts the same [ar
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  from ultralytics import YOLO
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  # Load a model
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- model = YOLO("yolo26n.pt")
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  # Train the model
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  train_results = model.train(
@@ -143,7 +141,7 @@ See YOLO [Python Docs](https://docs.ultralytics.com/usage/python/) for more exam
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  ## <div align="center">Models</div>
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- YOLO26 [Detect](https://docs.ultralytics.com/tasks/detect/), [Segment](https://docs.ultralytics.com/tasks/segment/) and [Pose](https://docs.ultralytics.com/tasks/pose/) models pretrained on the [COCO](https://docs.ultralytics.com/datasets/detect/coco/) dataset are available here, as well as YOLO26 [Classify](https://docs.ultralytics.com/tasks/classify/) models pretrained on the [ImageNet](https://docs.ultralytics.com/datasets/classify/imagenet/) dataset. [Track](https://docs.ultralytics.com/modes/track/) mode is available for all Detect, Segment and Pose models.
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  <img width="1024" src="https://raw.githubusercontent.com/ultralytics/assets/main/im/banner-tasks.png" alt="Ultralytics YOLO supported tasks">
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@@ -153,13 +151,13 @@ All [Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/cf
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  See [Detection Docs](https://docs.ultralytics.com/tasks/detect/) for usage examples with these models trained on [COCO](https://docs.ultralytics.com/datasets/detect/coco/), which include 80 pre-trained classes.
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- | Model | size<br><sup>(pixels) | mAP<sup>val<br>50-95 | mAP<sup>val<br>50-95(e2e) | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>T4 TensorRT10<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |
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- | ------------------------------------------------------------------------------------ | --------------------- | -------------------- | ------------------------- | ------------------------------ | ----------------------------------- | ------------------ | ----------------- |
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- | [YOLO26n](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo26n.pt) | 640 | 40.9 | 40.1 | 38.9 ± 0.7 | 1.7 ± 0.0 | 2.4 | 5.4 |
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- | [YOLO26s](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo26s.pt) | 640 | 48.6 | 47.8 | 87.2 ± 0.9 | 2.5 ± 0.0 | 9.5 | 20.7 |
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- | [YOLO26m](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo26m.pt) | 640 | 53.1 | 52.5 | 220.0 ± 1.4 | 4.7 ± 0.1 | 20.4 | 68.2 |
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- | [YOLO26l](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo26l.pt) | 640 | 55.0 | 54.4 | 286.2 ± 2.0 | 6.2 ± 0.2 | 24.8 | 86.4 |
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- | [YOLO26x](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo26x.pt) | 640 | 57.5 | 56.9 | 525.8 ± 4.0 | 11.8 ± 0.2 | 55.7 | 193.9 |
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  - **mAP<sup>val</sup>** values are for single-model single-scale on [COCO val2017](https://cocodataset.org/) dataset. <br>Reproduce by `yolo val detect data=coco.yaml device=0`
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  - **Speed** averaged over COCO val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance. <br>Reproduce by `yolo val detect data=coco.yaml batch=1 device=0|cpu`
@@ -170,13 +168,13 @@ See [Detection Docs](https://docs.ultralytics.com/tasks/detect/) for usage examp
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  See [Segmentation Docs](https://docs.ultralytics.com/tasks/segment/) for usage examples with these models trained on [COCO-Seg](https://docs.ultralytics.com/datasets/segment/coco/), which include 80 pre-trained classes.
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- | Model | size<br><sup>(pixels) | mAP<sup>box<br>50-95(e2e) | mAP<sup>mask<br>50-95(e2e) | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>T4 TensorRT10<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |
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- | -------------------------------------------------------------------------------------------- | --------------------- | ------------------------- | -------------------------- | ------------------------------ | ----------------------------------- | ------------------ | ----------------- |
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- | [YOLO26n-seg](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo26n-seg.pt) | 640 | 39.6 | 33.9 | 53.3 ± 0.5 | 2.1 ± 0.0 | 2.7 | 9.1 |
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- | [YOLO26s-seg](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo26s-seg.pt) | 640 | 47.3 | 40.0 | 118.4 ± 0.9 | 3.3 ± 0.0 | 10.4 | 34.2 |
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- | [YOLO26m-seg](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo26m-seg.pt) | 640 | 52.5 | 44.1 | 328.2 ± 2.4 | 6.7 ± 0.1 | 23.6 | 121.5 |
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- | [YOLO26l-seg](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo26l-seg.pt) | 640 | 54.4 | 45.5 | 387.0 ± 3.7 | 8.0 ± 0.1 | 28.0 | 139.8 |
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- | [YOLO26x-seg](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo26x-seg.pt) | 640 | 56.5 | 47.0 | 787.0 ± 6.8 | 16.4 ± 0.1 | 62.8 | 313.5 |
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  - **mAP<sup>val</sup>** values are for single-model single-scale on [COCO val2017](https://cocodataset.org/) dataset. <br>Reproduce by `yolo val segment data=coco-seg.yaml device=0`
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  - **Speed** averaged over COCO val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance. <br>Reproduce by `yolo val segment data=coco-seg.yaml batch=1 device=0|cpu`
@@ -187,13 +185,13 @@ See [Segmentation Docs](https://docs.ultralytics.com/tasks/segment/) for usage e
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  See [Classification Docs](https://docs.ultralytics.com/tasks/classify/) for usage examples with these models trained on [ImageNet](https://docs.ultralytics.com/datasets/classify/imagenet/), which include 1000 pretrained classes.
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- | Model | size<br><sup>(pixels) | acc<br><sup>top1 | acc<br><sup>top5 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>T4 TensorRT10<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) at 224 |
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  | -------------------------------------------------------------------------------------------- | --------------------- | ---------------- | ---------------- | ------------------------------ | ----------------------------------- | ------------------ | ------------------------ |
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- | [YOLO26n-cls](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo26n-cls.pt) | 224 | 71.4 | 90.1 | 5.0 ± 0.3 | 1.1 ± 0.0 | 2.8 | 0.5 |
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- | [YOLO26s-cls](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo26s-cls.pt) | 224 | 76.0 | 92.9 | 7.9 ± 0.2 | 1.3 ± 0.0 | 6.7 | 1.6 |
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- | [YOLO26m-cls](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo26m-cls.pt) | 224 | 78.1 | 94.2 | 17.2 ± 0.4 | 2.0 ± 0.0 | 11.6 | 4.9 |
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- | [YOLO26l-cls](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo26l-cls.pt) | 224 | 79.0 | 94.6 | 23.2 ± 0.3 | 2.8 ± 0.0 | 14.1 | 6.2 |
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- | [YOLO26x-cls](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo26x-cls.pt) | 224 | 79.9 | 95.0 | 41.4 ± 0.9 | 3.8 ± 0.0 | 29.6 | 13.6 |
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  - **acc** values are model accuracies on the [ImageNet](https://www.image-net.org/) dataset validation set. <br>Reproduce by `yolo val classify data=path/to/ImageNet device=0`
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  - **Speed** averaged over ImageNet val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance. <br>Reproduce by `yolo val classify data=path/to/ImageNet batch=1 device=0|cpu`
@@ -204,13 +202,13 @@ See [Classification Docs](https://docs.ultralytics.com/tasks/classify/) for usag
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  See [Pose Docs](https://docs.ultralytics.com/tasks/pose/) for usage examples with these models trained on [COCO-Pose](https://docs.ultralytics.com/datasets/pose/coco/), which include 1 pre-trained class, person.
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- | Model | size<br><sup>(pixels) | mAP<sup>pose<br>50-95(e2e) | mAP<sup>pose<br>50(e2e) | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>T4 TensorRT10<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |
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- | ---------------------------------------------------------------------------------------------- | --------------------- | -------------------------- | ----------------------- | ------------------------------ | ----------------------------------- | ------------------ | ----------------- |
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- | [YOLO26n-pose](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo26n-pose.pt) | 640 | 57.2 | 83.3 | 40.3 ± 0.5 | 1.8 ± 0.0 | 2.9 | 7.5 |
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- | [YOLO26s-pose](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo26s-pose.pt) | 640 | 63.0 | 86.6 | 85.3 ± 0.9 | 2.7 ± 0.0 | 10.4 | 23.9 |
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- | [YOLO26m-pose](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo26m-pose.pt) | 640 | 68.8 | 89.6 | 218.0 ± 1.5 | 5.0 ± 0.1 | 21.5 | 73.1 |
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- | [YOLO26l-pose](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo26l-pose.pt) | 640 | 70.4 | 90.5 | 275.4 ± 2.4 | 6.5 ± 0.1 | 25.9 | 91.3 |
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- | [YOLO26x-pose](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo26x-pose.pt) | 640 | 71.6 | 91.6 | 565.4 ± 3.0 | 12.2 ± 0.2 | 57.6 | 201.7 |
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  - **mAP<sup>val</sup>** values are for single-model single-scale on [COCO Keypoints val2017](https://cocodataset.org/) dataset. <br>Reproduce by `yolo val pose data=coco-pose.yaml device=0`
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  - **Speed** averaged over COCO val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance. <br>Reproduce by `yolo val pose data=coco-pose.yaml batch=1 device=0|cpu`
@@ -221,32 +219,51 @@ See [Pose Docs](https://docs.ultralytics.com/tasks/pose/) for usage examples wit
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  See [OBB Docs](https://docs.ultralytics.com/tasks/obb/) for usage examples with these models trained on [DOTAv1](https://docs.ultralytics.com/datasets/obb/dota-v2/#dota-v10/), which include 15 pre-trained classes.
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- | Model | size<br><sup>(pixels) | mAP<sup>test<br>50-95(e2e) | mAP<sup>test<br>50(e2e) | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>T4 TensorRT10<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |
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- | -------------------------------------------------------------------------------------------- | --------------------- | -------------------------- | ----------------------- | ------------------------------ | ----------------------------------- | ------------------ | ----------------- |
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- | [YOLO26n-obb](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo26n-obb.pt) | 1024 | 52.4 | 78.9 | 97.7 ± 0.9 | 2.8 ± 0.0 | 2.5 | 14.0 |
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- | [YOLO26s-obb](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo26s-obb.pt) | 1024 | 54.8 | 80.9 | 218.0 ± 1.4 | 4.9 ± 0.1 | 9.8 | 55.1 |
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- | [YOLO26m-obb](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo26m-obb.pt) | 1024 | 55.3 | 81.0 | 579.2 ± 3.8 | 10.2 ± 0.3 | 21.2 | 183.3 |
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- | [YOLO26l-obb](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo26l-obb.pt) | 1024 | 56.2 | 81.6 | 735.6 ± 3.1 | 13.0 ± 0.2 | 25.6 | 230.0 |
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- | [YOLO26x-obb](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo26x-obb.pt) | 1024 | 56.7 | 81.7 | 1485.7 ± 11.5 | 30.5 ± 0.9 | 57.6 | 516.5 |
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  - **mAP<sup>test</sup>** values are for single-model multiscale on [DOTAv1](https://captain-whu.github.io/DOTA/index.html) dataset. <br>Reproduce by `yolo val obb data=DOTAv1.yaml device=0 split=test` and submit merged results to [DOTA evaluation](https://captain-whu.github.io/DOTA/evaluation.html).
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  - **Speed** averaged over DOTAv1 val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance. <br>Reproduce by `yolo val obb data=DOTAv1.yaml batch=1 device=0|cpu`
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  </details>
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- ## 🧩 Integrations
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- Our key integrations with leading AI platforms extend the functionality of Ultralytics' offerings, enhancing tasks like dataset labeling, training, visualization, and model management. Discover how Ultralytics, in collaboration with partners like [Weights & Biases](https://docs.ultralytics.com/integrations/weights-biases/), [Comet ML](https://docs.ultralytics.com/integrations/comet/), [Roboflow](https://docs.ultralytics.com/integrations/roboflow/), and [Intel OpenVINO](https://docs.ultralytics.com/integrations/openvino/), can optimize your AI workflow. Explore more at [Ultralytics Integrations](https://docs.ultralytics.com/integrations/).
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- <a href="https://docs.ultralytics.com/integrations/" target="_blank">
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- <img width="100%" src="https://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png" alt="Ultralytics active learning integrations">
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- </a>
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  <br>
 
 
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  <br>
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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- | <a href="https://platform.ultralytics.com/ultralytics/yolo26"><img src="https://github.com/ultralytics/assets/raw/main/partners/logo-ultralytics-hub.png" width="40%" alt="Ultralytics Platform logo"></a><br>Ultralytics Platform 🌟 | <a href="https://docs.ultralytics.com/integrations/weights-biases/"><img src="https://github.com/ultralytics/assets/raw/main/partners/logo-wb.png" width="40%" alt="Weights & Biases logo"></a><br>Weights & Biases | <a href="https://docs.ultralytics.com/integrations/comet/"><img src="https://github.com/ultralytics/assets/raw/main/partners/logo-comet.png" width="40%" alt="Comet ML logo"></a><br>Comet | <a href="https://docs.ultralytics.com/integrations/neural-magic/"><img src="https://github.com/ultralytics/assets/raw/main/partners/logo-neuralmagic.png" width="40%" alt="Neural Magic logo"></a><br>Neural Magic |
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- | :---: | :---: | :---: | :---: |
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- | Streamline YOLO workflows: Label, train, and deploy effortlessly with [Ultralytics Platform](https://platform.ultralytics.com/ultralytics/yolo26). Try now! | Track experiments, hyperparameters, and results with [Weights & Biases](https://docs.ultralytics.com/integrations/weights-biases/). | Free forever, [Comet ML](https://docs.ultralytics.com/integrations/comet/) lets you save YOLO models, resume training, and interactively visualize predictions. | Run YOLO inference up to 6x faster with [Neural Magic DeepSparse](https://docs.ultralytics.com/integrations/neural-magic/). |
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251
  ## <div align="center">Contribute</div>
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14
  - ar
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  library_name: ultralytics
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  model-index:
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+ - name: ultralytics/yolo11
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  results:
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  - task:
20
  type: object-detection
 
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31
  <div align="center">
32
  <p>
33
+ <a href="https://www.ultralytics.com/blog/ultralytics-yolo11-has-arrived-redefine-whats-possible-in-ai" target="_blank">
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  <img width="100%" src="https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/banner-yolov8.png" alt="Ultralytics YOLO banner"></a>
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  </p>
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47
  <a href="https://www.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>
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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 Ultralytics on Gradient"></a>
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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 Ultralytics In Colab"></a>
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+ <a href="https://www.kaggle.com/models/ultralytics/yolo11" style="position: relative; top: -3px;"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open Ultralytics In Kaggle"></a>
51
  <a href="https://mybinder.org/v2/gh/ultralytics/ultralytics/HEAD?labpath=examples%2Ftutorial.ipynb" style="position: relative; top: -3px;"><img src="https://mybinder.org/badge_logo.svg" alt="Open Ultralytics In Binder"></a>
52
  </div>
53
  </div>
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  <br>
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+ [Ultralytics](https://www.ultralytics.com/) [YOLO11](https://github.com/ultralytics/ultralytics) is a cutting-edge, state-of-the-art (SOTA) model that builds upon the success of previous YOLO versions and introduces new features and improvements to further boost performance and flexibility. YOLO11 is designed to be fast, accurate, and easy to use, making it an excellent choice for a wide range of object detection and tracking, instance segmentation, image classification and pose estimation tasks.
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58
  We hope that the resources here will help you get the most out of YOLO. Please browse the Ultralytics <a href="https://docs.ultralytics.com/">Docs</a> for details, raise an issue on <a href="https://github.com/ultralytics/ultralytics/issues/new/choose">GitHub</a> for support, questions, or discussions, become a member of the Ultralytics <a href="https://discord.com/invite/ultralytics">Discord</a>, <a href="https://reddit.com/r/ultralytics">Reddit</a> and <a href="https://community.ultralytics.com/">Forums</a>!
59
 
60
  To request an Enterprise License please complete the form at [Ultralytics Licensing](https://www.ultralytics.com/license).
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62
+ <img width="100%" src="https://github.com/user-attachments/assets/a311a4ed-bbf2-43b5-8012-5f183a28a845" alt="YOLO11 performance plots"></a>
 
 
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  <div style="display: flex; justify-content: center; flex-wrap: wrap;">
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  <a href="https://github.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-github.png" width="35%" alt="Ultralytics GitHub"></a>
 
101
  YOLO may be used directly in the Command Line Interface (CLI) with a `yolo` command:
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103
  ```bash
104
+ yolo predict model=yolo11n.pt source='https://ultralytics.com/images/bus.jpg'
105
  ```
106
 
107
  `yolo` can be used for a variety of tasks and modes and accepts additional arguments, i.e. `imgsz=640`. See the YOLO [CLI Docs](https://docs.ultralytics.com/usage/cli/) for examples.
 
114
  from ultralytics import YOLO
115
 
116
  # Load a model
117
+ model = YOLO("yolo11n.pt")
118
 
119
  # Train the model
120
  train_results = model.train(
 
141
 
142
  ## <div align="center">Models</div>
143
 
144
+ YOLO11 [Detect](https://docs.ultralytics.com/tasks/detect/), [Segment](https://docs.ultralytics.com/tasks/segment/) and [Pose](https://docs.ultralytics.com/tasks/pose/) models pretrained on the [COCO](https://docs.ultralytics.com/datasets/detect/coco/) dataset are available here, as well as YOLO11 [Classify](https://docs.ultralytics.com/tasks/classify/) models pretrained on the [ImageNet](https://docs.ultralytics.com/datasets/classify/imagenet/) dataset. [Track](https://docs.ultralytics.com/modes/track/) mode is available for all Detect, Segment and Pose models.
145
 
146
  <img width="1024" src="https://raw.githubusercontent.com/ultralytics/assets/main/im/banner-tasks.png" alt="Ultralytics YOLO supported tasks">
147
 
 
151
 
152
  See [Detection Docs](https://docs.ultralytics.com/tasks/detect/) for usage examples with these models trained on [COCO](https://docs.ultralytics.com/datasets/detect/coco/), which include 80 pre-trained classes.
153
 
154
+ | Model | size<br><sup>(pixels) | mAP<sup>val<br>50-95 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>T4 TensorRT10<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |
155
+ | ------------------------------------------------------------------------------------ | --------------------- | -------------------- | ------------------------------ | ----------------------------------- | ------------------ | ----------------- |
156
+ | [YOLO11n](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n.pt) | 640 | 39.5 | 56.1 ± 0.8 | 1.5 ± 0.0 | 2.6 | 6.5 |
157
+ | [YOLO11s](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11s.pt) | 640 | 47.0 | 90.0 ± 1.2 | 2.5 ± 0.0 | 9.4 | 21.5 |
158
+ | [YOLO11m](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11m.pt) | 640 | 51.5 | 183.2 ± 2.0 | 4.7 ± 0.1 | 20.1 | 68.0 |
159
+ | [YOLO11l](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11l.pt) | 640 | 53.4 | 238.6 ± 1.4 | 6.2 ± 0.1 | 25.3 | 86.9 |
160
+ | [YOLO11x](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11x.pt) | 640 | 54.7 | 462.8 ± 6.7 | 11.3 ± 0.2 | 56.9 | 194.9 |
161
 
162
  - **mAP<sup>val</sup>** values are for single-model single-scale on [COCO val2017](https://cocodataset.org/) dataset. <br>Reproduce by `yolo val detect data=coco.yaml device=0`
163
  - **Speed** averaged over COCO val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance. <br>Reproduce by `yolo val detect data=coco.yaml batch=1 device=0|cpu`
 
168
 
169
  See [Segmentation Docs](https://docs.ultralytics.com/tasks/segment/) for usage examples with these models trained on [COCO-Seg](https://docs.ultralytics.com/datasets/segment/coco/), which include 80 pre-trained classes.
170
 
171
+ | Model | size<br><sup>(pixels) | mAP<sup>box<br>50-95 | mAP<sup>mask<br>50-95 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>T4 TensorRT10<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |
172
+ | -------------------------------------------------------------------------------------------- | --------------------- | -------------------- | --------------------- | ------------------------------ | ----------------------------------- | ------------------ | ----------------- |
173
+ | [YOLO11n-seg](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n-seg.pt) | 640 | 38.9 | 32.0 | 65.9 ± 1.1 | 1.8 ± 0.0 | 2.9 | 10.4 |
174
+ | [YOLO11s-seg](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11s-seg.pt) | 640 | 46.6 | 37.8 | 117.6 ± 4.9 | 2.9 ± 0.0 | 10.1 | 35.5 |
175
+ | [YOLO11m-seg](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11m-seg.pt) | 640 | 51.5 | 41.5 | 281.6 ± 1.2 | 6.3 ± 0.1 | 22.4 | 123.3 |
176
+ | [YOLO11l-seg](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11l-seg.pt) | 640 | 53.4 | 42.9 | 344.2 ± 3.2 | 7.8 ± 0.2 | 27.6 | 142.2 |
177
+ | [YOLO11x-seg](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11x-seg.pt) | 640 | 54.7 | 43.8 | 664.5 ± 3.2 | 15.8 ± 0.7 | 62.1 | 319.0 |
178
 
179
  - **mAP<sup>val</sup>** values are for single-model single-scale on [COCO val2017](https://cocodataset.org/) dataset. <br>Reproduce by `yolo val segment data=coco-seg.yaml device=0`
180
  - **Speed** averaged over COCO val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance. <br>Reproduce by `yolo val segment data=coco-seg.yaml batch=1 device=0|cpu`
 
185
 
186
  See [Classification Docs](https://docs.ultralytics.com/tasks/classify/) for usage examples with these models trained on [ImageNet](https://docs.ultralytics.com/datasets/classify/imagenet/), which include 1000 pretrained classes.
187
 
188
+ | Model | size<br><sup>(pixels) | acc<br><sup>top1 | acc<br><sup>top5 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>T4 TensorRT10<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) at 640 |
189
  | -------------------------------------------------------------------------------------------- | --------------------- | ---------------- | ---------------- | ------------------------------ | ----------------------------------- | ------------------ | ------------------------ |
190
+ | [YOLO11n-cls](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n-cls.pt) | 224 | 70.0 | 89.4 | 5.0 ± 0.3 | 1.1 ± 0.0 | 1.6 | 3.3 |
191
+ | [YOLO11s-cls](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11s-cls.pt) | 224 | 75.4 | 92.7 | 7.9 ± 0.2 | 1.3 ± 0.0 | 5.5 | 12.1 |
192
+ | [YOLO11m-cls](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11m-cls.pt) | 224 | 77.3 | 93.9 | 17.2 ± 0.4 | 2.0 ± 0.0 | 10.4 | 39.3 |
193
+ | [YOLO11l-cls](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11l-cls.pt) | 224 | 78.3 | 94.3 | 23.2 ± 0.3 | 2.8 ± 0.0 | 12.9 | 49.4 |
194
+ | [YOLO11x-cls](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11x-cls.pt) | 224 | 79.5 | 94.9 | 41.4 ± 0.9 | 3.8 ± 0.0 | 28.4 | 110.4 |
195
 
196
  - **acc** values are model accuracies on the [ImageNet](https://www.image-net.org/) dataset validation set. <br>Reproduce by `yolo val classify data=path/to/ImageNet device=0`
197
  - **Speed** averaged over ImageNet val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance. <br>Reproduce by `yolo val classify data=path/to/ImageNet batch=1 device=0|cpu`
 
202
 
203
  See [Pose Docs](https://docs.ultralytics.com/tasks/pose/) for usage examples with these models trained on [COCO-Pose](https://docs.ultralytics.com/datasets/pose/coco/), which include 1 pre-trained class, person.
204
 
205
+ | Model | size<br><sup>(pixels) | mAP<sup>pose<br>50-95 | mAP<sup>pose<br>50 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>T4 TensorRT10<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |
206
+ | ---------------------------------------------------------------------------------------------- | --------------------- | --------------------- | ------------------ | ------------------------------ | ----------------------------------- | ------------------ | ----------------- |
207
+ | [YOLO11n-pose](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n-pose.pt) | 640 | 50.0 | 81.0 | 52.4 ± 0.5 | 1.7 ± 0.0 | 2.9 | 7.6 |
208
+ | [YOLO11s-pose](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11s-pose.pt) | 640 | 58.9 | 86.3 | 90.5 ± 0.6 | 2.6 ± 0.0 | 9.9 | 23.2 |
209
+ | [YOLO11m-pose](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11m-pose.pt) | 640 | 64.9 | 89.4 | 187.3 ± 0.8 | 4.9 ± 0.1 | 20.9 | 71.7 |
210
+ | [YOLO11l-pose](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11l-pose.pt) | 640 | 66.1 | 89.9 | 247.7 ± 1.1 | 6.4 ± 0.1 | 26.2 | 90.7 |
211
+ | [YOLO11x-pose](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11x-pose.pt) | 640 | 69.5 | 91.1 | 488.0 ± 13.9 | 12.1 ± 0.2 | 58.8 | 203.3 |
212
 
213
  - **mAP<sup>val</sup>** values are for single-model single-scale on [COCO Keypoints val2017](https://cocodataset.org/) dataset. <br>Reproduce by `yolo val pose data=coco-pose.yaml device=0`
214
  - **Speed** averaged over COCO val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance. <br>Reproduce by `yolo val pose data=coco-pose.yaml batch=1 device=0|cpu`
 
219
 
220
  See [OBB Docs](https://docs.ultralytics.com/tasks/obb/) for usage examples with these models trained on [DOTAv1](https://docs.ultralytics.com/datasets/obb/dota-v2/#dota-v10/), which include 15 pre-trained classes.
221
 
222
+ | Model | size<br><sup>(pixels) | mAP<sup>test<br>50 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>T4 TensorRT10<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |
223
+ | -------------------------------------------------------------------------------------------- | --------------------- | ------------------ | ------------------------------ | ----------------------------------- | ------------------ | ----------------- |
224
+ | [YOLO11n-obb](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n-obb.pt) | 1024 | 78.4 | 117.6 ± 0.8 | 4.4 ± 0.0 | 2.7 | 17.2 |
225
+ | [YOLO11s-obb](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11s-obb.pt) | 1024 | 79.5 | 219.4 ± 4.0 | 5.1 ± 0.0 | 9.7 | 57.5 |
226
+ | [YOLO11m-obb](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11m-obb.pt) | 1024 | 80.9 | 562.8 ± 2.9 | 10.1 ± 0.4 | 20.9 | 183.5 |
227
+ | [YOLO11l-obb](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11l-obb.pt) | 1024 | 81.0 | 712.5 ± 5.0 | 13.5 ± 0.6 | 26.2 | 232.0 |
228
+ | [YOLO11x-obb](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11x-obb.pt) | 1024 | 81.3 | 1408.6 ± 7.7 | 28.6 ± 1.0 | 58.8 | 520.2 |
229
 
230
  - **mAP<sup>test</sup>** values are for single-model multiscale on [DOTAv1](https://captain-whu.github.io/DOTA/index.html) dataset. <br>Reproduce by `yolo val obb data=DOTAv1.yaml device=0 split=test` and submit merged results to [DOTA evaluation](https://captain-whu.github.io/DOTA/evaluation.html).
231
  - **Speed** averaged over DOTAv1 val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance. <br>Reproduce by `yolo val obb data=DOTAv1.yaml batch=1 device=0|cpu`
232
 
233
  </details>
234
 
235
+ ## <div align="center">Integrations</div>
236
 
237
+ Our key integrations with leading AI platforms extend the functionality of Ultralytics' offerings, enhancing tasks like dataset labeling, training, visualization, and model management. Discover how Ultralytics, in collaboration with [Roboflow](https://roboflow.com/?ref=ultralytics), ClearML, [Comet](https://bit.ly/yolov8-readme-comet), Neural Magic and [OpenVINO](https://docs.ultralytics.com/integrations/openvino/), can optimize your AI workflow.
238
 
 
 
 
239
  <br>
240
+ <a href="https://www.ultralytics.com/hub" target="_blank">
241
+ <img width="100%" src="https://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png" alt="Ultralytics active learning integrations"></a>
242
  <br>
243
+ <br>
244
+
245
+ <div style="display: flex; justify-content: center; flex-wrap: wrap; gap: 50px;">
246
+ <a href="https://roboflow.com/?ref=ultralytics">
247
+ <img src="https://github.com/ultralytics/assets/raw/main/partners/logo-roboflow.png" width="50%" alt="Roboflow logo"></a>
248
+ <a href="https://clear.ml/">
249
+ <img src="https://github.com/ultralytics/assets/raw/main/partners/logo-clearml.png" width="50%" alt="ClearML logo"></a>
250
+ <a href="https://bit.ly/yolov8-readme-comet">
251
+ <img src="https://github.com/ultralytics/assets/raw/main/partners/logo-comet.png" width="50%" alt="Comet ML logo"></a>
252
+ <a href="https://bit.ly/yolov5-neuralmagic">
253
+ <img src="https://github.com/ultralytics/assets/raw/main/partners/logo-neuralmagic.png" width="50%" alt="NeuralMagic logo"></a>
254
+ </div>
255
+
256
+
257
+ | Roboflow | ClearML ⭐ NEW | Comet ⭐ NEW | Neural Magic ⭐ NEW |
258
+ | :--------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------: |
259
+ | Label and export your custom datasets directly to YOLO11 for training with [Roboflow](https://roboflow.com/?ref=ultralytics) | Automatically track, visualize and even remotely train YOLO11 using [ClearML](https://clear.ml/) (open-source!) | Free forever, [Comet](https://bit.ly/yolov5-readme-comet) lets you save YOLO11 models, resume training, and interactively visualize and debug predictions | Run YOLO11 inference up to 6x faster with [Neural Magic DeepSparse](https://bit.ly/yolov5-neuralmagic) |
260
+
261
+ ## <div align="center">Ultralytics HUB</div>
262
+
263
+ Experience seamless AI with [Ultralytics HUB](https://www.ultralytics.com/hub) ⭐, the all-in-one solution for data visualization, YOLO11 🚀 model training and deployment, without any coding. Transform images into actionable insights and bring your AI visions to life with ease using our cutting-edge platform and user-friendly [Ultralytics App](https://www.ultralytics.com/app-install). Start your journey for **Free** now!
264
 
265
+ <a href="https://www.ultralytics.com/hub" target="_blank">
266
+ <img width="100%" src="https://github.com/ultralytics/assets/raw/main/im/ultralytics-hub.png" alt="Ultralytics HUB preview image"></a>
 
267
 
268
  ## <div align="center">Contribute</div>
269