Keypoint Detection
ultralytics
ONNX
TensorRT
human pose estimation
pose-estimation
yolo26
yolo26x-pose
human-pose
Instructions to use select-ai/pose-detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ultralytics
How to use select-ai/pose-detection with ultralytics:
# Couldn't find a valid YOLO version tag. # Replace XX with the correct version. from ultralytics import YOLOvXX model = YOLOvXX.from_pretrained("select-ai/pose-detection") source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - TensorRT
How to use select-ai/pose-detection with TensorRT:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
|
Download docs/train.log.md from select-ai/pose-detection: direct link, hf CLI and curl.
- Browser
- Download file 1.5 kB
-
https://huggingface.co/select-ai/pose-detection/resolve/main/docs/train.log.md
- Command line
-
hf download hf://select-ai/pose-detection/docs/train.log.md
-
curl -L -o train.log.md https://huggingface.co/select-ai/pose-detection/resolve/main/docs/train.log.md
1.5 kB
Training and evaluation record
v1 provenance
- Model: YOLO26x-Pose
- Version:
v1 - Status:
pretrained(inventory status); model card status remainsexperimentaluntil independent validation completes - Upstream trainer/developer: Ultralytics
- Upstream model reference: Ultralytics YOLO26x-Pose
- Select AI training run: none
- Select AI fine-tuning run: none
- Selected checkpoint: YOLO26x-Pose upstream pretrained checkpoint
- Checkpoint path:
models/yolo26x-pose.pt - Provenance script:
scripts/train.py(records provenance only; does not launch training or download datasets)
The v1 package contains the upstream pretrained YOLO26x-Pose artifact from Ultralytics. Select AI did not train or fine-tune the published checkpoint. Model specifications for the upstream checkpoint are taken from the Ultralytics platform reference linked above.
Output contract (v1)
- Per-person feature vector: 51 floats (17 COCO keypoints ×
x,y,confidence) - Per-person bounding box:
box_xyxy = [x1, y1, x2, y2] - Downstream consumers assemble bbox + feature vector into a pandas DataFrame
Reference benchmark record
The retained benchmark record documents the upstream reference performance for YOLO26x-Pose at 960 × 960 on the COCO Keypoints validation set.
| Model | Input | Dataset | mAP50-95 | mAP50 |
|---|---|---|---|---|
| YOLO26x-Pose | 960 × 960 | COCO Keypoints | 71.6% | 91.6% |