YOLO26-M-obb

Ultralytics YOLO26 is the latest evolution in the YOLO series, engineered from the ground up for edge and low-power devices. This is the Oriented Bounding Box (OBB) variant optimized for detecting rotated objects in aerial and satellite imagery.

Model Specifications

Property	Value
Input Size	1024 pixels
mAP Test (50-95, e2e)	55.3
mAP Test (50, e2e)	81.0
CPU Speed (ONNX)	579.2 ms
T4 TensorRT10 Speed	10.2 ms
Parameters	21.2M
FLOPs	183.3B

Key Features

The architecture of YOLO26 is guided by three core principles:

Simplicity: YOLO26 is a native end-to-end model, producing predictions directly without the need for non-maximum suppression (NMS). By eliminating this post-processing step, inference becomes faster, lighter, and easier to deploy in real-world systems.

Deployment Efficiency: The end-to-end design cuts out an entire stage of the pipeline, dramatically simplifying integration, reducing latency, and making deployment more robust across diverse environments.

Training Innovation: YOLO26 introduces the MuSGD optimizer, a hybrid of SGD and Muon — inspired by Moonshot AI's Kimi K2 breakthroughs in LLM training. This optimizer brings enhanced stability and faster convergence.

Refined OBB Features

• Specialized Angle Loss: Improved detection accuracy for square-shaped objects
• Optimized OBB Decoding: Resolves boundary discontinuity issues
• DFL Removal: Simplified inference and broader hardware compatibility
• Up to 43% Faster CPU Inference: Optimized for edge computing

Usage

Install ultralytics with pip install ultralytics.

Download the model.

from huggingface_hub import hf_hub_download

model_path = hf_hub_download(repo_id="openvision/yolo26-m-obb", filename="model.pt")

Infer.

from ultralytics import YOLO
from PIL import Image
import requests

model = YOLO(model_path)

url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
image = Image.open(requests.get(url, stream=True).raw)

# Run inference with the YOLO26m-obb model on the image
results = model.predict(image)

Documentation

For more information, see the official YOLO26 documentation.