# Ultralytics YOLO11 Released 🎉

**URL:** <https://community.ultralytics.com/t/ultralytics-yolo11-released/301>\
**Category:** Discussion\
**Tags:** showcase, ultralytics-official\
**Created:** [September 30, 2024, 12:11pm UTC](https://community.ultralytics.com/t/ultralytics-yolo11-released/301 "2024-09-30T12:11:39Z")\
**Posts on this page:** 2\
**Page:** 1

<div class="post-metadata">

**Author:** ![muhammadrizwanm](https://sea1.discourse-cdn.com/flex001/user_avatar/community.ultralytics.com/muhammadrizwanm/32/71_2.png) [@muhammadrizwanm](https://community.ultralytics.com/u/muhammadrizwanm)\
**Post date:** [September 30, 2024, 12:11pm UTC](https://community.ultralytics.com/t/ultralytics-yolo11-released/301/1 "2024-09-30T12:11:39Z")

</div>

Just tested the Ultralytics YOLO11 models, performing well! 🤩🚀

I experimented with various tasks like detection, segmentation, prediction, and pose estimation using random images, and the results look fantastic! 😃

In case you want to give it a try `yolo predict source='path/to/image.png`

Learn more ➡ [YOLO11 - Ultralytics YOLO Docs](https://docs.ultralytics.com/models/yolo11/)

| Object Detection | Pose Estimation | Object Detection | Image Segmentation |
| --- | --- | --- | --- |
| ![Image 1](https://us1.discourse-cdn.com/flex001/uploads/ultralytics1/original/1X/d267f88be53021099bd6b17f36eb0be11e44b1b4.jpeg) | ![Image 2](https://us1.discourse-cdn.com/flex001/uploads/ultralytics1/original/1X/df33b6d682202f1f50729d3ae68331122f35889d.jpeg) | ![Image 3](https://us1.discourse-cdn.com/flex001/uploads/ultralytics1/original/1X/809e3d208e097b6877ecce2cae262c372b8bb45d.jpeg) | ![Image 4](https://us1.discourse-cdn.com/flex001/uploads/ultralytics1/original/1X/bb0af1114503a981c520835e0b218531e54e6848.jpeg) |

---

<div class="post-metadata">

**Author:** ![glenn-jocher](https://sea1.discourse-cdn.com/flex001/user_avatar/community.ultralytics.com/glenn-jocher/32/83_2.png) [@glenn-jocher](https://community.ultralytics.com/u/glenn-jocher)\
**Post date:** [October 1, 2024, 5:22pm UTC](https://community.ultralytics.com/t/ultralytics-yolo11-released/301/2 "2024-10-01T17:22:20Z")

</div>

More details here!

### 🛠 R&D Highlights

- **25 Open-Source Models:** YOLO11 introduces **25 models** across **5 sizes** and **5 tasks** , ensuring there’s an optimized model for any use case.
- **Accuracy Boost:** YOLO11n achieves up to a **2.2% higher mAP (37.3 → 39.5)** on COCO object detection tasks compared to YOLOv8n.
- **Efficiency & Speed:** YOLO11 uses **up to 22% fewer parameters** than YOLOv8 and provides up to **2% faster inference speeds**. Optimized for edge applications and resource-constrained environments.

The focus of YOLO11 is on refining architecture to improve performance while reducing computational requirements—a great fit for those who need both precision and speed.

### 📊 YOLO11 Benchmarks

The improvements are consistent across all model sizes, providing a noticeable upgrade for current YOLO users.

| Model | YOLOv8 mAP (%) | YOLO11 mAP (%) | YOLOv8 Params (M) | YOLO11 Params (M) | Improvement |
| --- | --- | --- | --- | --- | --- |
| **YOLOn** | 37.3 | 39.5 | 3.2 | 2.6 | +2.2% mAP |
| **YOLOs** | 44.9 | 47.0 | 11.2 | 9.4 | +2.1% mAP |
| **YOLOm** | 50.2 | 51.5 | 25.9 | 20.1 | +1.3% mAP |
| **YOLOl** | 52.9 | 53.4 | 43.7 | 25.3 | +0.5% mAP |
| **YOLOx** | 53.9 | 54.7 | 68.2 | 56.9 | +0.8% mAP |

### 💡 Versatile Task Support

YOLO11 extends the capabilities of the YOLO series to cover multiple computer vision tasks:

- [**Detection**](https://docs.ultralytics.com/tasks/detect/): Quickly detect and localize objects.
- [**Instance Segmentation**](https://docs.ultralytics.com/tasks/segment/): Get pixel-level object insights.
- [**Pose Estimation**](https://docs.ultralytics.com/tasks/pose/): Track key points for pose analysis.
- [**Oriented Object Detection (OBB)**](https://docs.ultralytics.com/tasks/obb/): Detect objects with orientation angles.
- [**Classification**](https://docs.ultralytics.com/tasks/classify/): Classify images into categories.

### 🔧 Quick Start Example

If you’re already using the Ultralytics package, upgrading to YOLO11 is easy. Install the latest package:

```bash
pip install ultralytics>=8.3.0

```

Then, load a pre-trained YOLO11 model and run inference on an image:

```python
from ultralytics import YOLO

# Load the YOLO11 model
model = YOLO("yolo11n.pt")

# Run inference on an image
results = model("path/to/image.jpg")

# Display results
results[0].show()

```

These few lines of code are all you need to start using YOLO11 for your real-time computer vision needs.

### 📦 Access and Get Involved

YOLO11 is open-source and designed to integrate smoothly into various workflows, from edge devices to cloud platforms. You can explore the models and contribute at [GitHub - ultralytics/ultralytics: Ultralytics YOLO 🚀](https://github.com/ultralytics/ultralytics).

Check it out, see how it fits into your projects, and let us know your feedback!