YOLO26-N-pose
Ultralytics YOLO26 is the latest evolution in the YOLO series, engineered from the ground up for edge and low-power devices. This is the pose estimation variant optimized for human keypoint detection.
Model Specifications
| Property | Value |
|---|---|
| Input Size | 640 pixels |
| mAP Pose (50-95, e2e) | 57.2 |
| mAP Pose (50, e2e) | 83.3 |
| CPU Speed (ONNX) | 40.3 ms |
| T4 TensorRT10 Speed | 1.8 ms |
| Parameters | 2.9M |
| FLOPs | 7.5B |
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.
Precision Pose Estimation Features
- Residual Log-Likelihood Estimation (RLE): More accurate keypoint localization
- Optimized Decoding: Increased inference speed for pose estimation
- 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-n-pose", 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 YOLO26n-pose model on the image
results = model.predict(image)
Documentation
For more information, see the official YOLO26 documentation.