Ultralytics ORG

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Ultralytics creates cutting-edge, state-of-the-art (SOTA) YOLO models 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, tracking, instance segmentation, semantic segmentation, image classification, and pose estimation tasks.

Find detailed documentation in the Ultralytics Docs. Get support via GitHub Issues. Join discussions on Discord, Reddit, and the Ultralytics Community Forums!

Request an Enterprise License for commercial use at Ultralytics Licensing.

📄 Documentation

See below for quickstart installation and usage examples. For comprehensive guidance on training, validation, prediction, and deployment, refer to our full Ultralytics Docs.

Install

Install the ultralytics package, including all requirements, in a Python>=3.8 environment with PyTorch>=1.8.

  

bash
pip install ultralytics

For alternative installation methods, including Conda, Docker, and building from source via Git, please consult the Quickstart Guide.

  

Usage

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 for more examples.

Python

Ultralytics YOLO can also be integrated directly into your Python projects. It accepts the same configuration arguments 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.

✨ Models

Ultralytics supports a wide range of YOLO models, from early versions like YOLOv3 to the latest YOLO26. The tables below showcase YOLO26 models pretrained on COCO for Detection, Segmentation, and Pose Estimation. Semantic Segmentation models are pretrained on Cityscapes, and Classification models are pretrained on ImageNet. Tracking mode is compatible with Detection, Segmentation, and Pose models. All Models download automatically from the latest Ultralytics release on first use.

Detection (COCO)

Explore the Detection Docs for usage examples. These models are trained on the COCO dataset, featuring 80 object classes.

| Model | size(pixels) | mAPval50-95 | mAPval50-95(e2e) | SpeedCPU ONNX(ms) | SpeedT4 TensorRT10(ms) | params(M) | FLOPs(B) |
| ------------------------------------------------------------------------------------ | --------------------------- | -------------------------- | ------------------------------- | ------------------------------------ | ----------------------------------------- | ------------------------ | ----------------------- |
| YOLO26n | 640 | 40.9 | 40.1 | 38.9 ± 0.7 | 1.7 ± 0.0 | 2.4 | 5.4 |
| YOLO26s | 640 | 48.6 | 47.8 | 87.2 ± 0.9 | 2.5 ± 0.0 | 9.5 | 20.7 |
| YOLO26m | 640 | 53.1 | 52.5 | 220.0 ± 1.4 | 4.7 ± 0.1 | 20.4 | 68.2 |
| YOLO26l | 640 | 55.0 | 54.4 | 286.2 ± 2.0 | 6.2 ± 0.2 | 24.8 | 86.4 |
| YOLO26x | 640 | 57.5 | 56.9 | 525.8 ± 4.0 | 11.8 ± 0.2 | 55.7 | 193.9 |

• mAPval values refer to single-model single-scale performance on the COCO val2017 dataset. See YOLO Performance Metrics for details. Reproduce with yolo val detect data=coco.yaml device=0
• Speed metrics are averaged over COCO val images using an Amazon EC2 P4d instance. CPU speeds measured with ONNX export. GPU speeds measured with TensorRT export. Reproduce with yolo val detect data=coco.yaml batch=1 device=0|cpu

Segmentation (COCO)

Refer to the Segmentation Docs for usage examples. These models are trained on COCO-Seg, including 80 classes.

| Model | size(pixels) | mAPbox50-95(e2e) | mAPmask50-95(e2e) | SpeedCPU ONNX(ms) | SpeedT4 TensorRT10(ms) | params(M) | FLOPs(B) |
| -------------------------------------------------------------------------------------------- | --------------------------- | ------------------------------- | -------------------------------- | ------------------------------------ | ----------------------------------------- | ------------------------ | ----------------------- |
| YOLO26n-seg | 640 | 39.6 | 33.9 | 53.3 ± 0.5 | 2.1 ± 0.0 | 2.7 | 9.1 |
| YOLO26s-seg | 640 | 47.3 | 40.0 | 118.4 ± 0.9 | 3.3 ± 0.0 | 10.4 | 34.2 |
| YOLO26m-seg | 640 | 52.5 | 44.1 | 328.2 ± 2.4 | 6.7 ± 0.1 | 23.6 | 121.5 |
| YOLO26l-seg | 640 | 54.4 | 45.5 | 387.0 ± 3.7 | 8.0 ± 0.1

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