YOLO26-S-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)	63.0
mAP Pose (50, e2e)	86.6
CPU Speed (ONNX)	85.3 ms
T4 TensorRT10 Speed	2.7 ms
Parameters	10.4M
FLOPs	23.9B

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-s-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 YOLO26s-pose model on the image
results = model.predict(image)

Documentation

For more information, see the official YOLO26 documentation.