YOLO26 now available

Train, Deploy, and Scale Ultralytics YOLO Models

The end-to-end platform for building production-ready computer vision models. Train Ultralytics YOLO models, manage datasets, and deploy with one click.

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Featured Models

State-of-the-art Ultralytics YOLO models ready for your next project

Newest SOTA Ultralytics models, released January 2026. Smaller, faster, and more accurate than previous versions with native end-to-end inference.

25 models1.1 GB2d ago

yolo26nyolo26xyolo26m+22

YOLO11

public

11

Released September 2024. Current stable release and the recommended choice for all use cases. Best balance of speed, accuracy, and ease of use across all vision tasks.

10 models462.6 MB2d ago

yolo11xyolo11syolo11n+7

YOLOv8

public

8

Released January 2023. Proven and widely adopted with extensive community support. Introduced the unified Python API and CLI that became the Ultralytics standard.

10 models599.3 MB2d ago

yolov8xyolov8syolov8n+7

YOLOv5

public

5

Released June 2020. The foundational PyTorch implementation, battle-tested across millions of deployments. Ideal for teams with existing YOLOv5 workflows.

5 models359.3 MB2d ago

yolov5luyolov5muyolov5su+2

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API Access

Deploy this model to get a dedicated API endpoint

Deploy your model to get a dedicated inference endpoint with API key authentication. Once deployed, replace the placeholder URL and API key below with your actual deployment values. See the Deploy docs for more details.

Note: This is example code. Deploy your model first to get your endpoint URL and API key.

import requests

# Replace with your deployment URL and API key from Settings
url = "https://your-deployment-url.run.app/predict"
headers = {"Authorization": "Bearer YOUR_API_KEY"}

# Inference parameters
data = {"conf": 0.25, "iou": 0.7, "imgsz": 640}

# Send image for inference
with open("image.jpg", "rb") as f:
    response = requests.post(url, headers=headers, data=data, files={"file": f})

print(response.json())