Duplicate from Ultralytics/YOLO26

Co-authored-by: Onuralp SEZER <onuralpszr@users.noreply.huggingface.co>

.gitattributes

@@ -0,0 +1,35 @@
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,287 @@
+---
+license: agpl-3.0
+language:
+- en
+- zh
+- ja
+- ru
+- de
+- fr
+- es
+- pt
+- tr
+- vi
+- ar
+library_name: ultralytics
+model-index:
+- name: ultralytics/yolo26
+  results:
+  - task:
+      type: object-detection
+    dataset:
+      type: merve/coco
+      name: coco
+      split: validation
+    metrics:
+      - type: precision  # since mAP@0.5:0.95 is not available on hf.co/metrics
+        value: 57.5
+        name: mAP@0.5:0.95
+---
+<div align="center">
+  <p>
+    <a href="https://platform.ultralytics.com/?utm_source=huggingface&utm_medium=referral&utm_campaign=yolo26&utm_content=banner" target="_blank">
+      <img width="100%" src="https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/banner-yolov8.png" alt="Ultralytics YOLO banner"></a>
+  </p>
+  <p style="margin: 3px 0;">
+    <a href="https://docs.ultralytics.com/zh/">中文</a> | <a href="https://docs.ultralytics.com/ko/">한국어</a> | <a href="https://docs.ultralytics.com/ja/">日本語</a> | <a href="https://docs.ultralytics.com/ru/">Русский</a> | <a href="https://docs.ultralytics.com/de/">Deutsch</a> | <a href="https://docs.ultralytics.com/fr/">Français</a> | <a href="https://docs.ultralytics.com/es">Español</a> | <a href="https://docs.ultralytics.com/pt/">Português</a> | <a href="https://docs.ultralytics.com/tr/">Türkçe</a> | <a href="https://docs.ultralytics.com/vi/">Tiếng Việt</a> | <a href="https://docs.ultralytics.com/ar/">العربية</a>
+  </p>
+
+  <div style="display: flex; flex-wrap: wrap; justify-content: center; align-items: center; gap: 3px; margin-top: 3px;">
+    <a href="https://github.com/ultralytics/ultralytics/actions/workflows/ci.yml"><img src="https://github.com/ultralytics/ultralytics/actions/workflows/ci.yml/badge.svg" alt="Ultralytics CI"></a>
+    <a href="https://pepy.tech/projects/ultralytics" style="position: relative; top: -3px;"><img src="https://static.pepy.tech/badge/ultralytics" alt="Ultralytics Downloads"></a>
+    <a href="https://zenodo.org/badge/latestdoi/264818686" style="position: relative; top: -3px;"><img src="https://zenodo.org/badge/264818686.svg" alt="Ultralytics YOLO Citation"></a>
+    <a href="https://discord.com/invite/ultralytics"><img alt="Ultralytics Discord" src="https://img.shields.io/discord/1089800235347353640?logo=discord&logoColor=white&label=Discord&color=blue"></a>
+    <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>
+    <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>
+  </div>
+  <div style="display: flex; flex-wrap: wrap; justify-content: center; align-items: center; gap: 3px; margin-top: 3px;">
+    <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>
+    <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>
+    <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>
+    <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>
+  </div>
+</div>
+
+[Ultralytics](https://www.ultralytics.com/) creates cutting-edge, state-of-the-art (SOTA) [YOLO models](https://www.ultralytics.com/yolo) built on years of foundational research in computer vision and AI. Constantly updated for performance and flexibility, our models are **fast**, **accurate**, and **easy to use**. They excel at [object detection](https://docs.ultralytics.com/tasks/detect/), [tracking](https://docs.ultralytics.com/modes/track/), [instance segmentation](https://docs.ultralytics.com/tasks/segment/), [image classification](https://docs.ultralytics.com/tasks/classify/), and [pose estimation](https://docs.ultralytics.com/tasks/pose/) tasks.
+
+Find detailed documentation in the [Ultralytics Docs](https://docs.ultralytics.com/). Get support via [GitHub Issues](https://github.com/ultralytics/ultralytics/issues/new/choose). Join discussions on [Discord](https://discord.com/invite/ultralytics), [Reddit](https://www.reddit.com/r/ultralytics/), and the [Ultralytics Community Forums](https://community.ultralytics.com/)!
+
+Request an Enterprise License for commercial use at [Ultralytics Licensing](https://www.ultralytics.com/license).
+
+<a href="https://docs.ultralytics.com/models/yolo26/?utm_source=huggingface&utm_medium=referral&utm_campaign=yolo26&utm_content=benchmark_graph" target="_blank">
+  <img width="100%" src="https://raw.githubusercontent.com/ultralytics/assets/refs/heads/main/yolo/performance-comparison.png" alt="YOLO26 performance plots">
+</a>
+<div align="center" style="display: flex; flex-wrap: wrap; justify-content: center; align-items: center; gap: 20px;">
+  <a href="https://github.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-github.png" width="24" alt="Ultralytics GitHub"></a>
+  <a href="https://www.linkedin.com/company/ultralytics/"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-linkedin.png" width="24" alt="Ultralytics LinkedIn"></a>
+  <a href="https://twitter.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-twitter.png" width="24" alt="Ultralytics Twitter"></a>
+  <a href="https://www.youtube.com/ultralytics?sub_confirmation=1"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-youtube.png" width="24" alt="Ultralytics YouTube"></a>
+  <a href="https://www.tiktok.com/@ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-tiktok.png" width="24" alt="Ultralytics TikTok"></a>
+  <a href="https://ultralytics.com/bilibili"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-bilibili.png" width="24" alt="Ultralytics BiliBili"></a>
+  <a href="https://discord.com/invite/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-discord.png" width="24" alt="Ultralytics Discord"></a>
+</div>
+</div>
+
+## <div align="center">📄 Documentation</div>
+
+See below for quickstart installation and usage examples. For comprehensive guidance on training, validation, prediction, and deployment, refer to our full [Ultralytics Docs](https://docs.ultralytics.com/).
+
+<details open>
+<summary>Install</summary>
+
+Install the `ultralytics` package, including all [requirements](https://github.com/ultralytics/ultralytics/blob/main/pyproject.toml), in a [**Python>=3.8**](https://www.python.org/) environment with [**PyTorch>=1.8**](https://pytorch.org/get-started/locally/).
+
+<div align="left" style="display: flex; flex-wrap: wrap; justify-content: left; align-items: center; gap: 5px;">
+  <a href="https://pypi.org/project/ultralytics/"><img src="https://img.shields.io/pypi/v/ultralytics?logo=pypi&logoColor=white" alt="PyPI - Version"></a>
+  <a href="https://pepy.tech/project/ultralytics"><img src="https://static.pepy.tech/badge/ultralytics" style="position: relative; top: -3px;" alt="Downloads"></a>
+  <a href="https://pypi.org/project/ultralytics/"><img src="https://img.shields.io/pypi/pyversions/ultralytics?logo=python&logoColor=gold" alt="PyPI - Python Version"></a>
+</div>
+
+```bash
+pip install ultralytics
+```
+
+For alternative installation methods, including [Conda](https://anaconda.org/conda-forge/ultralytics), [Docker](https://hub.docker.com/r/ultralytics/ultralytics), and building from source via Git, please consult the [Quickstart Guide](https://docs.ultralytics.com/quickstart/).
+
+<div align="left" style="display: flex; flex-wrap: wrap; justify-content: left; align-items: center; gap: 5px;">
+  <a href="https://anaconda.org/conda-forge/ultralytics"><img src="https://img.shields.io/conda/vn/conda-forge/ultralytics?logo=condaforge" alt="Conda Version"></a>
+  <a href="https://hub.docker.com/r/ultralytics/ultralytics"><img src="https://img.shields.io/docker/v/ultralytics/ultralytics?sort=semver&logo=docker" alt="Docker Image Version"></a>
+  <a href="https://hub.docker.com/r/ultralytics/ultralytics"><img src="https://img.shields.io/docker/pulls/ultralytics/ultralytics?logo=docker" alt="Ultralytics Docker Pulls"></a>
+</div>
+
+</details>
+
+<details open>
+<summary>Usage</summary>
+
+### CLI
+
+You can use Ultralytics YOLO directly from the Command Line Interface (CLI) with the `yolo` command:
+
+```bash
+# Predict using a pretrained YOLO model (e.g., YOLO26n) on an image
+yolo predict model=yolo26n.pt source='https://ultralytics.com/images/bus.jpg'
+```
+
+The `yolo` command supports various tasks and modes, accepting additional arguments like `imgsz=640`. Explore the YOLO [CLI Docs](https://docs.ultralytics.com/usage/cli/) for more examples.
+
+### Python
+
+Ultralytics YOLO can also be integrated directly into your Python projects. It accepts the same [configuration arguments](https://docs.ultralytics.com/usage/cfg/) as the CLI:
+
+```python
+from ultralytics import YOLO
+
+# Load a pretrained YOLO26n model
+model = YOLO("yolo26n.pt")
+
+# Train the model on the COCO8 dataset for 100 epochs
+train_results = model.train(
+    data="coco8.yaml",  # Path to dataset configuration file
+    epochs=100,  # Number of training epochs
+    imgsz=640,  # Image size for training
+    device="cpu",  # Device to run on (e.g., 'cpu', 0, [0,1,2,3])
+)
+
+# Evaluate the model's performance on the validation set
+metrics = model.val()
+
+# Perform object detection on an image
+results = model("path/to/image.jpg")  # Predict on an image
+results[0].show()  # Display results
+
+# Export the model to ONNX format for deployment
+path = model.export(format="onnx")  # Returns the path to the exported model
+```
+
+Discover more examples in the YOLO [Python Docs](https://docs.ultralytics.com/usage/python/).
+
+</details>
+
+## <div align="center">✨ Models</div>
+
+Ultralytics supports a wide range of YOLO models, from early versions like [YOLOv3](https://docs.ultralytics.com/models/yolov3/) to the latest [Ultralytics YOLO26](https://platform.ultralytics.com/ultralytics/yolo26?utm_source=huggingface&utm_medium=referral&utm_campaign=yolo26&utm_content=contextual_model_link). The tables below showcase [Ultralytics YOLO26](https://platform.ultralytics.com/ultralytics/yolo26?utm_source=huggingface&utm_medium=referral&utm_campaign=yolo26&utm_content=contextual_model_link) models pretrained on the [COCO](https://docs.ultralytics.com/datasets/detect/coco/) dataset for [Detection](https://docs.ultralytics.com/tasks/detect/), [Segmentation](https://docs.ultralytics.com/tasks/segment/), and [Pose Estimation](https://docs.ultralytics.com/tasks/pose/). Additionally, [Classification](https://docs.ultralytics.com/tasks/classify/) models pretrained on the [ImageNet](https://docs.ultralytics.com/datasets/classify/imagenet/) dataset are available. [Tracking](https://docs.ultralytics.com/modes/track/) mode is compatible with all Detection, Segmentation, and Pose models. All [Models](https://docs.ultralytics.com/models/) are automatically downloaded from the latest Ultralytics [release](https://github.com/ultralytics/assets/releases) upon first use.
+
+<img width="1024" src="https://raw.githubusercontent.com/ultralytics/assets/main/im/banner-tasks.png" alt="Ultralytics YOLO supported tasks">
+
+<details open><summary>Detection (COCO)</summary>
+
+Explore the [Detection Docs](https://docs.ultralytics.com/tasks/detect/) for usage examples. These models are trained on the [COCO dataset](https://cocodataset.org/), featuring 80 object classes.
+
+| 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) |
+| ------------------------------------------------------------------------------------ | --------------------- | -------------------- | ------------------------- | ------------------------------ | ----------------------------------- | ------------------ | ----------------- |
+| [YOLO26n](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26n.pt) | 640 | 40.9 | 40.1 | 38.9 ± 0.7 | 1.7 ± 0.0 | 2.4 | 5.4 |
+| [YOLO26s](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26s.pt) | 640 | 48.6 | 47.8 | 87.2 ± 0.9 | 2.5 ± 0.0 | 9.5 | 20.7 |
+| [YOLO26m](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26m.pt) | 640 | 53.1 | 52.5 | 220.0 ± 1.4 | 4.7 ± 0.1 | 20.4 | 68.2 |
+| [YOLO26l](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26l.pt) | 640 | 55.0 | 54.4 | 286.2 ± 2.0 | 6.2 ± 0.2 | 24.8 | 86.4 |
+| [YOLO26x](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26x.pt) | 640 | 57.5 | 56.9 | 525.8 ± 4.0 | 11.8 ± 0.2 | 55.7 | 193.9 |
+
+- **mAP<sup>val</sup>** values refer to single-model single-scale performance on the [COCO val2017](https://cocodataset.org/) dataset. See [YOLO Performance Metrics](https://docs.ultralytics.com/guides/yolo-performance-metrics/) for details. <br>Reproduce with `yolo val detect data=coco.yaml device=0`
+- **Speed** metrics are averaged over COCO val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance. CPU speeds measured with [ONNX](https://onnx.ai/) export. GPU speeds measured with [TensorRT](https://developer.nvidia.com/tensorrt) export. <br>Reproduce with `yolo val detect data=coco.yaml batch=1 device=0|cpu`
+
+</details>
+
+<details><summary>Segmentation (COCO)</summary>
+
+Refer to the [Segmentation Docs](https://docs.ultralytics.com/tasks/segment/) for usage examples. These models are trained on [COCO-Seg](https://docs.ultralytics.com/datasets/segment/coco/), including 80 classes.
+
+| 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) |
+| -------------------------------------------------------------------------------------------- | --------------------- | ------------------------- | -------------------------- | ------------------------------ | ----------------------------------- | ------------------ | ----------------- |
+| [YOLO26n-seg](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26n-seg.pt) | 640 | 39.6 | 33.9 | 53.3 ± 0.5 | 2.1 ± 0.0 | 2.7 | 9.1 |
+| [YOLO26s-seg](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26s-seg.pt) | 640 | 47.3 | 40.0 | 118.4 ± 0.9 | 3.3 ± 0.0 | 10.4 | 34.2 |
+| [YOLO26m-seg](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26m-seg.pt) | 640 | 52.5 | 44.1 | 328.2 ± 2.4 | 6.7 ± 0.1 | 23.6 | 121.5 |
+| [YOLO26l-seg](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26l-seg.pt) | 640 | 54.4 | 45.5 | 387.0 ± 3.7 | 8.0 ± 0.1 | 28.0 | 139.8 |
+| [YOLO26x-seg](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26x-seg.pt) | 640 | 56.5 | 47.0 | 787.0 ± 6.8 | 16.4 ± 0.1 | 62.8 | 313.5 |
+
+- **mAP<sup>val</sup>** values are for single-model single-scale on the [COCO val2017](https://cocodataset.org/) dataset. See [YOLO Performance Metrics](https://docs.ultralytics.com/guides/yolo-performance-metrics/) for details. <br>Reproduce with `yolo val segment data=coco.yaml device=0`
+- **Speed** metrics are averaged over COCO val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance. CPU speeds measured with [ONNX](https://onnx.ai/) export. GPU speeds measured with [TensorRT](https://developer.nvidia.com/tensorrt) export. <br>Reproduce with `yolo val segment data=coco.yaml batch=1 device=0|cpu`
+
+</details>
+
+<details><summary>Classification (ImageNet)</summary>
+
+Consult the [Classification Docs](https://docs.ultralytics.com/tasks/classify/) for usage examples. These models are trained on [ImageNet](https://docs.ultralytics.com/datasets/classify/imagenet/), covering 1000 classes.
+
+| 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 |
+| -------------------------------------------------------------------------------------------- | --------------------- | ---------------- | ---------------- | ------------------------------ | ----------------------------------- | ------------------ | ------------------------ |
+| [YOLO26n-cls](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26n-cls.pt) | 224 | 71.4 | 90.1 | 5.0 ± 0.3 | 1.1 ± 0.0 | 2.8 | 0.5 |
+| [YOLO26s-cls](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26s-cls.pt) | 224 | 76.0 | 92.9 | 7.9 ± 0.2 | 1.3 ± 0.0 | 6.7 | 1.6 |
+| [YOLO26m-cls](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26m-cls.pt) | 224 | 78.1 | 94.2 | 17.2 ± 0.4 | 2.0 ± 0.0 | 11.6 | 4.9 |
+| [YOLO26l-cls](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26l-cls.pt) | 224 | 79.0 | 94.6 | 23.2 ± 0.3 | 2.8 ± 0.0 | 14.1 | 6.2 |
+| [YOLO26x-cls](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26x-cls.pt) | 224 | 79.9 | 95.0 | 41.4 ± 0.9 | 3.8 ± 0.0 | 29.6 | 13.6 |
+
+- **acc** values represent model accuracy on the [ImageNet](https://www.image-net.org/) dataset validation set. <br>Reproduce with `yolo val classify data=path/to/ImageNet device=0`
+- **Speed** metrics are averaged over ImageNet val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance. CPU speeds measured with [ONNX](https://onnx.ai/) export. GPU speeds measured with [TensorRT](https://developer.nvidia.com/tensorrt) export. <br>Reproduce with `yolo val classify data=path/to/ImageNet batch=1 device=0|cpu`
+
+</details>
+
+<details><summary>Pose (COCO)</summary>
+
+See the [Pose Estimation Docs](https://docs.ultralytics.com/tasks/pose/) for usage examples. These models are trained on [COCO-Pose](https://docs.ultralytics.com/datasets/pose/coco/), focusing on the 'person' class.
+
+| 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) |
+| ---------------------------------------------------------------------------------------------- | --------------------- | -------------------------- | ----------------------- | ------------------------------ | ----------------------------------- | ------------------ | ----------------- |
+| [YOLO26n-pose](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26n-pose.pt) | 640 | 57.2 | 83.3 | 40.3 ± 0.5 | 1.8 ± 0.0 | 2.9 | 7.5 |
+| [YOLO26s-pose](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26s-pose.pt) | 640 | 63.0 | 86.6 | 85.3 ± 0.9 | 2.7 ± 0.0 | 10.4 | 23.9 |
+| [YOLO26m-pose](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26m-pose.pt) | 640 | 68.8 | 89.6 | 218.0 ± 1.5 | 5.0 ± 0.1 | 21.5 | 73.1 |
+| [YOLO26l-pose](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26l-pose.pt) | 640 | 70.4 | 90.5 | 275.4 ± 2.4 | 6.5 ± 0.1 | 25.9 | 91.3 |
+| [YOLO26x-pose](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26x-pose.pt) | 640 | 71.6 | 91.6 | 565.4 ± 3.0 | 12.2 ± 0.2 | 57.6 | 201.7 |
+
+- **mAP<sup>val</sup>** values are for single-model single-scale on the [COCO Keypoints val2017](https://docs.ultralytics.com/datasets/pose/coco/) dataset. See [YOLO Performance Metrics](https://docs.ultralytics.com/guides/yolo-performance-metrics/) for details. <br>Reproduce with `yolo val pose data=coco-pose.yaml device=0`
+- **Speed** metrics are averaged over COCO val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance. CPU speeds measured with [ONNX](https://onnx.ai/) export. GPU speeds measured with [TensorRT](https://developer.nvidia.com/tensorrt) export. <br>Reproduce with `yolo val pose data=coco-pose.yaml batch=1 device=0|cpu`
+
+</details>
+
+<details><summary>Oriented Bounding Boxes (DOTAv1)</summary>
+
+Check the [OBB Docs](https://docs.ultralytics.com/tasks/obb/) for usage examples. These models are trained on [DOTAv1](https://docs.ultralytics.com/datasets/obb/dota-v2/#dota-v10/), including 15 classes.
+
+| 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) |
+| -------------------------------------------------------------------------------------------- | --------------------- | -------------------------- | ----------------------- | ------------------------------ | ----------------------------------- | ------------------ | ----------------- |
+| [YOLO26n-obb](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26n-obb.pt) | 1024 | 52.4 | 78.9 | 97.7 ± 0.9 | 2.8 ± 0.0 | 2.5 | 14.0 |
+| [YOLO26s-obb](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26s-obb.pt) | 1024 | 54.8 | 80.9 | 218.0 ± 1.4 | 4.9 ± 0.1 | 9.8 | 55.1 |
+| [YOLO26m-obb](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26m-obb.pt) | 1024 | 55.3 | 81.0 | 579.2 ± 3.8 | 10.2 ± 0.3 | 21.2 | 183.3 |
+| [YOLO26l-obb](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26l-obb.pt) | 1024 | 56.2 | 81.6 | 735.6 ± 3.1 | 13.0 ± 0.2 | 25.6 | 230.0 |
+| [YOLO26x-obb](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26x-obb.pt) | 1024 | 56.7 | 81.7 | 1485.7 ± 11.5 | 30.5 ± 0.9 | 57.6 | 516.5 |
+
+- **mAP<sup>test</sup>** values are for single-model multiscale performance on the [DOTAv1 test set](https://captain-whu.github.io/DOTA/dataset.html). <br>Reproduce by `yolo val obb data=DOTAv1.yaml device=0 split=test` and submit merged results to the [DOTA evaluation server](https://captain-whu.github.io/DOTA/evaluation.html).
+- **Speed** metrics are averaged over [DOTAv1 val images](https://docs.ultralytics.com/datasets/obb/dota-v2/#dota-v10) using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance. CPU speeds measured with [ONNX](https://onnx.ai/) export. GPU speeds measured with [TensorRT](https://developer.nvidia.com/tensorrt) export. <br>Reproduce by `yolo val obb data=DOTAv1.yaml batch=1 device=0|cpu`
+
+</details>
+
+## <div align="center">🧩 Integrations</div>
+
+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/).
+
+<a href="https://docs.ultralytics.com/integrations/" target="_blank">
+    <img width="100%" src="https://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png" alt="Ultralytics active learning integrations">
+</a>
+<br>
+<br>
+
+| <a href="https://platform.ultralytics.com/ultralytics/yolo26?utm_source=huggingface&utm_medium=referral&utm_campaign=yolo26&utm_content=contextual_model_link"><img src="https://cdn.jsdelivr.net/gh/ultralytics/assets@main/logo/Ultralytics-logomark-color.png" width="30%" 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 |
+| :---: | :---: | :---: | :---: |
+| Streamline Ultralytics YOLO workflows: Label, train, and deploy effortlessly with [Ultralytics Platform](https://platform.ultralytics.com/ultralytics/yolo26?utm_source=huggingface&utm_medium=referral&utm_campaign=yolo26&utm_content=contextual_model_link). 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 Ultralytics YOLO models, resume training, and interactively visualize predictions. | Run Ultralytics YOLO inference up to 6x faster with [Neural Magic DeepSparse](https://docs.ultralytics.com/integrations/neural-magic/). |
+
+## <div align="center">🤝 Contribute</div>
+
+We thrive on community collaboration! Ultralytics YOLO wouldn't be the SOTA framework it is without contributions from developers like you. Please see our [Contributing Guide](https://docs.ultralytics.com/help/contributing/) to get started. We also welcome your feedback—share your experience by completing our [Survey](https://www.ultralytics.com/survey?utm_source=github&utm_medium=social&utm_campaign=Survey). A huge **Thank You** 🙏 to everyone who contributes!
+
+<!-- SVG image from https://opencollective.com/ultralytics/contributors.svg?width=990 -->
+
+<a href="https://github.com/ultralytics/ultralytics/graphs/contributors">
+<img width="100%" src="https://github.com/ultralytics/assets/raw/main/im/image-contributors.png" alt="Ultralytics open-source contributors"></a>
+
+We look forward to your contributions to help make the Ultralytics ecosystem even better!
+
+## <div align="center">📜 License</div>
+
+Ultralytics offers two licensing options to suit different needs:
+
+- **AGPL-3.0 License**: This [OSI-approved](https://opensource.org/license/agpl-v3) open-source license is perfect for students, researchers, and enthusiasts. It encourages open collaboration and knowledge sharing. See the [LICENSE](https://github.com/ultralytics/ultralytics/blob/main/LICENSE) file for full details.
+- **Ultralytics Enterprise License**: Designed for commercial use, this license allows for the seamless integration of Ultralytics software and AI models into commercial products and services, bypassing the open-source requirements of AGPL-3.0. If your use case involves commercial deployment, please contact us via [Ultralytics Licensing](https://www.ultralytics.com/license).
+
+## <div align="center">📞 Contact</div>
+
+For bug reports and feature requests related to Ultralytics software, please visit [GitHub Issues](https://github.com/ultralytics/ultralytics/issues). For questions, discussions, and community support, join our active communities on [Discord](https://discord.com/invite/ultralytics), [Reddit](https://www.reddit.com/r/ultralytics/), and the [Ultralytics Community Forums](https://community.ultralytics.com/). We're here to help with all things Ultralytics!
+
+<br>
+<div align="center" style="display: flex; flex-wrap: wrap; justify-content: center; align-items: center; gap: 20px;">
+  <a href="https://github.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-github.png" width="24" alt="Ultralytics GitHub"></a>
+  <a href="https://www.linkedin.com/company/ultralytics/"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-linkedin.png" width="24" alt="Ultralytics LinkedIn"></a>
+  <a href="https://twitter.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-twitter.png" width="24" alt="Ultralytics Twitter"></a>
+  <a href="https://www.youtube.com/ultralytics?sub_confirmation=1"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-youtube.png" width="24" alt="Ultralytics YouTube"></a>
+  <a href="https://www.tiktok.com/@ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-tiktok.png" width="24" alt="Ultralytics TikTok"></a>
+  <a href="https://ultralytics.com/bilibili"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-bilibili.png" width="24" alt="Ultralytics BiliBili"></a>
+  <a href="https://discord.com/invite/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-discord.png" width="24" alt="Ultralytics Discord"></a>
+</div>

yolo26l-cls.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7a3aebb7de6f5d79f0153da627a5f68bd3d7df30e648c0fdb7f9217986ee129e
+size 28535928

yolo26l-obb.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8674b0c24bf68aab5eb45009e0ac3808ce432237edf8cb5c50ae2191cb263a2b
+size 57263057

yolo26l-pose.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ad33da8a29ea5772318c4c980844e47b56792d2b63815ad4e8e09c078c7d1abf
+size 57995961

yolo26l-seg.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:636024306410afa1732692322fba57d22ea2b1c2f07613fcee131a93d7dd380c
+size 63700037

yolo26l.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9fe3c544f2b19bebad7ea41e76d7ad3d88b7c2f10d11d24430c5311f6b32db26
+size 53211173

yolo26m-cls.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9f6546f33a70d910e2cd6dcca5265c4617b5670b19a1f287c39e99258bade01a
+size 23477398

yolo26m-obb.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:23e0630f66857cf4b87535f6e705b065f1e8a33603640b8e61ace85b75312903
+size 48363653

yolo26m-pose.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2fbf16367022256a226035695c5c389384c6706e8bb8ab8fcd0e7976f05443c4
+size 49036866

yolo26m-seg.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:16b636f04e8fb6a325b3370f22dc5e5535ff473e384f4d041fd28d788f6ee9f5
+size 54750385

yolo26m.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:401cea9ab23ad19246ff7744859816bc599f350e93c9dd30367b6f0a0745d0b7
+size 44255705

yolo26n-cls.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0dd6f8dbc448870ac98a3cbb7156f923f7ce21fed3755d4019169ffffd279e81
+size 5786434

yolo26n-obb.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6f51c78197aacda4a33be77294065a9001675fb893f56227a179731b53dbd2b0
+size 5907357

yolo26n-pose.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:eb3bb8268828aeaf515cec23a4bfafd793944a86fe9af94ba7823609c14522a9
+size 7878574

yolo26n-seg.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:361fbfabab285c3237700b6bb91d7ecfa602cd945fffda8dbe1242829b71e73f
+size 6719965

yolo26n.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9b09cc8bf347f0fc8a5f7657480587f25db09b34bf33b0652110fb03a8ad4fef
+size 5544453

yolo26s-cls.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:816790029d5df3fef358f03c8144b96339d8824ee25577aeda8be0963e5c5f09
+size 13622082

yolo26s-obb.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:38dbd72ef6804f9bbbea7ad20f486e6ca6e093c8cd9bc857207a846565bd6e0b
+size 21701632

yolo26s-pose.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a083adb42303728ae14c4bd6bd56d80da46f82fb2564dbd6f31dcc92ea321646
+size 24151790

yolo26s-seg.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3da1d83e31caec96f9300eb4064f4f62882c133c7c264d63dfe61a7c197837a4
+size 23467933

yolo26s.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:646f8bc3fe0a656803d95c294f7852321748cb29d13466a1af8862e2db384a1b
+size 20422725

yolo26x-cls.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ee88a0c71e9596cdfdbb71892725929cb4489b874d24fb581d20bffe79724386
+size 59610168

yolo26x-obb.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d7b1d805b0ec149890efde89119ef9d60481566ec83166052c04b5d89d9e3cb7
+size 126937426

yolo26x-pose.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:08ed9e01d22a6f248b04f2f9992016aca9a32250b9ab57057d886a09d026700d
+size 126242553

yolo26x-seg.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:92b3de0065766a17180d6219858717dc9d03cdce8a3ca9576c97fd75aabb64f3
+size 142129861

yolo26x.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9fdd44a31c504547ffb81d2c6d9e6dac3493c8eaa8b0398d3f43bae6c7003e92
+size 118667365