YOLO26 - Object Detection

YOLO26 is the latest addition to the Ultralytics YOLO object detection model series. It achieves superior speed and accuracy compared to prior models in the YOLO series.

Overview

YOLO26 for object detection removes unnecessary complexity while integrating targeted innovations. Key features include:

• NMS-free end-to-end inference - Removing non-maximum suppression helps achieve lower inference latencies.
• DFL removal - Distribution Focal Loss removed for simpler export and broader edge compatibility
• MuSGD optimizer - Hybrid SGD/Muon optimizer for more stable training
• ProgLoss + STAL - Improved loss functions for better small-object detection
• Multiple model sizes - From nano to extra-large variants

License

AGPL-3.0

Commercial Licensing

• AGPL-3.0: Free for open-source projects. Requires derivative works to be open-sourced.
• Paid Roboflow customers: Automatically get access to use any YOLO26 models trained on or uploaded to the Roboflow platform for commercial use.
• Free Roboflow customers: Can use YOLO26 via the serverless hosted API, or commercially self-hosted with a paid plan.

Learn more: Roboflow Licensing | YOLO26 License Details

Pre-trained Model IDs

All pre-trained YOLO26 object detection models are trained on the COCO dataset (80 classes) and are open access (no API key required).

Model Size	640×640
Nano	yolo26n
Small	yolo26s
Medium	yolo26m
Large	yolo26l
Extra-Large	yolo26x

Custom model ID format: project-url/version (e.g., my-project-abc123/2)

Supported Backends

Backend	Extras Required
onnx	onnx-cpu, onnx-cu12, onnx-cu118, onnx-jp6-cu126
torch-script	torch-cpu, torch-cu118, torch-cu124, torch-cu126, torch-cu128, torch-jp6-cu126

Roboflow Platform Compatibility

Feature	Supported
Training	✅ Train custom models on Roboflow
Upload Weights	✅ Upload pre-trained weights
Serverless API (v2)	✅ Deploy via hosted API
Workflows	✅ Use in Workflows via Object Detection block
Edge Deployment (Jetson)	✅ Deploy on NVIDIA Jetson devices
Self-Hosting	✅ Deploy with inference-models

Usage Example

import cv2
import supervision as sv
from inference_models import AutoModel

# Load your custom model (requires Roboflow API key)
model = AutoModel.from_pretrained(
    "my-project-abc123/2",
    api_key="your_roboflow_api_key"
)
image = cv2.imread("path/to/image.jpg")

# Run inference and convert to supervision Detections
predictions = model(image)
detections = predictions[0].to_supervision()

# Annotate image
bounding_box_annotator = sv.BoxAnnotator()
annotated_image = bounding_box_annotator.annotate(image, detections)

# Save or display
cv2.imwrite("annotated.jpg", annotated_image)