# ultralytics

> Ultralytics YOLO 🚀 for SOTA object detection, multi-object tracking, instance segmentation, pose estimation and image classification.

- **URL**: https://www.freshcrate.ai/projects/ultralytics
- **Author**: pypi
- **Category**: RAG & Memory
- **Latest version**: `v8.4.60` (2026-06-01)
- **License**: AGPL-3.0
- **Source**: https://github.com/ultralytics/ultralytics/issues
- **Homepage**: https://pypi.org/project/ultralytics/
- **Language**: Python
- **GitHub**: 56,253 stars, 10,833 forks
- **Registry**: pypi (`ultralytics`)
- **Tags**: `ai`, `computer-vision`, `deep-learning`, `dl`, `machine-learning`, `ml`, `pypi`, `yolo`, `yolov3`

## Description

<div align="center">
  <p>
    <a href="https://platform.ultralytics.com/?utm_source=github&utm_medium=referral&utm_campaign=platform_launch&utm_content=banner&utm_term=ultralytics_github" target="_blank">
      <img width="100%" src="https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/banner-yolov8.png" alt="Ultralytics YOLO banner"></a>
  </p>

[中文](https://docs.ultralytics.com/zh/) | [한국어](https://docs.ultralytics.com/ko/) | [日本語](https://docs.ultralytics.com/ja/) | [Русский](https://docs.ultralytics.com/ru/) | [Deutsch](https://docs.ultralytics.com/de/) | [Français](https://docs.ultralytics.com/fr/) | [Español](https://docs.ultralytics.com/es) | [Português](https://docs.ultralytics.com/pt/) | [Türkçe](https://docs.ultralytics.com/tr/) | [Tiếng Việt](https://docs.ultralytics.com/vi/) | [العربية](https://docs.ultralytics.com/ar/) <br>

<div>
    <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://clickpy.clickhouse.com/dashboard/ultralytics"><img src="https://static.pepy.tech/badge/ultralytics" alt="Ultralytics Downloads"></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>
    <br>
    <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"><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"><img src="https://mybinder.org/badge_logo.svg" alt="Open Ultralytics In Binder"></a>
</div>
</div>
<br>

[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://platform.ultralytics.com/ultralytics/yolo26" 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">
  <a href="https://github.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-github.png" width="2%" alt="Ultralytics GitHub"></a>
  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="space">
  <a href="https://www.linkedin.com/company/ultralytics/"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-linkedin.png" width="2%" alt="Ultralytics LinkedIn"></a>
  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="space">
  <a href="https://twitter.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-twitter.png" width="2%" alt="Ultralytics Twitter"></a>
  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="space">
  <a href="https://www.youtube.com/ultralytics?sub_confirmation=1"><img s

## Recent releases

| Version | Date | Urgency | Changes |
| --- | --- | --- | --- |
| `v8.4.60` | 2026-06-01 | High | ## 🌟 Summary Ultralytics `v8.4.60` is mainly about **adding ONNX INT8 export** 🎉, making it easier to create **smaller, faster deployment models** with built-in calibration support, while also including a few helpful export, training, and documentation fixes.  ## 📊 Key Changes - 🚀 **Major new feature: ONNX `int8=True` export**   - You can now export models like **YOLO26** to **INT8 ONNX** using ONNX Runtime static quantization.   - This uses the same familiar export flow as other INT8 format |
| `v8.4.56` | 2026-05-27 | High | ## 🌟 Summary Ultralytics `v8.4.56` improves **QNN export reliability** by fixing compatibility with newer `onnxruntime-qnn` packages, especially on **Linux x86-64**. 🚀  ## 📊 Key Changes - **Fixed QNN export for built-in provider wheels** 🔧     Ultralytics now detects whether `onnxruntime-qnn` already includes `QNNExecutionProvider` internally, instead of always trying to register it as a separate plugin.  - **Avoids export failures on some Linux setups** 🐧     This prevents a known failure |
| `v8.4.53` | 2026-05-22 | High | ## 🌟 Summary Ultralytics `v8.4.53` mainly improves **training reliability on NVIDIA GPUs** by automatically recovering from more CUDA memory-related failures, while also polishing **semantic segmentation stability**, **documentation clarity**, and **CI robustness** 🚀  ## 📊 Key Changes - **Smarter GPU memory recovery during training** 🧠⚡     The biggest change in this release, from PR #24569 by @glenn-jocher, expands the existing auto-retry logic for large-batch GPU training.     - Previously |
| `v8.4.51` | 2026-05-15 | High | ## 🌟 Summary Ultralytics `v8.4.51` focuses mainly on **better training traceability and clearer deployment/docs updates** 📦📝, with the most important change adding the **Git commit message** to training metadata so models are easier to track, reproduce, and audit.  ## 📊 Key Changes - **Training metadata now includes the Git commit message** 🧾     The headline update from @glenn-jocher adds the current commit subject into:   - saved checkpoints as `git.message`   - Platform training environm |
| `v8.4.50` | 2026-05-13 | High | ## 🌟 Summary Ultralytics 8.4.50 is mainly a deployment-focused release 🚀, led by a new **DeepX export and inference integration** that makes it easier to run YOLO models on **DeepX NPU edge hardware**, along with a few quality-of-life fixes for **tuning reliability**, **mixed-precision model fusion**, and **RT-DETR documentation**.  ## 📊 Key Changes - **New DeepX export support added** 🧠⚡     You can now export Ultralytics models directly with `format="deepx"` as part of the normal export wo |
| `v8.4.48` | 2026-05-08 | High | ## 🌟 Summary Ultralytics `v8.4.48` is a **stability-focused release** that mainly improves training reliability on Ultralytics Platform and makes failure cases much clearer, with supporting fixes for benchmarking and reporting. 🛠️✅  ## 📊 Key Changes - **(Top priority) Platform training edge-case fixes** from PR #24431 by @glenn-jocher:   - Added guards for **empty semantic-mask batches** to prevent crashes in segmentation workflows. 🎭   - Added safe handling for **empty RLE keypoint masks** |
| `v8.4.47` | 2026-05-06 | High | ## 🌟 Summary Ultralytics **v8.4.47** is a reliability-focused release that fixes a key CLI bug for heatmap colormaps 🎨, while also improving RT-DETR post-processing, remote checkpoint loading, Edge TPU export behavior, and several stability edge cases across loaders and I/O ✅.  ## 📊 Key Changes - **🔥 Most important (current PR #24219 by @raimbekovm): CLI colormap parsing fixed for Solutions Heatmap**   - Commands like `colormap=cv2.COLORMAP_INFERNO` now work as documented.   - Previously, th |
| `v8.4.46` | 2026-05-01 | High | ## 🌟 Summary Ultralytics **v8.4.46** is a stability-focused release that primarily fixes a key **multi-scale training edge case** 🔧, while also improving export reliability, hardware support clarity, and documentation quality across YOLO workflows.  ## 📊 Key Changes - **🚨 Priority fix (PR #24394 by @glenn-jocher): Multi-scale training minimum size clamp**   - Multi-scale random resizing now enforces a **safe lower bound** of at least one model stride.   - Added a **regression test** to preve |
| `v8.4.42` | 2026-04-27 | High | ## 🌟 Summary Ultralytics `v8.4.42` focuses on **more reliable training under GPU memory pressure** 🧠💪, plus several **stability, security, export, and documentation improvements** that make YOLO workflows smoother for both developers and end users.  ## 📊 Key Changes - **Top priority (current PR #24360 by @glenn-jocher): Better OOM recovery during training** 🚀     - When CUDA runs out of memory in early training, temporary tensors (`batch`, `loss`, `preds`) and trainer loss state are now exp |
| `8.4.41` | 2026-04-21 | Low | Imported from PyPI (8.4.41) |

## Citation

- HTML: https://www.freshcrate.ai/projects/ultralytics
- Markdown: https://www.freshcrate.ai/projects/ultralytics.md
- Dependencies JSON: https://www.freshcrate.ai/api/projects/ultralytics/deps

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