metadata
license: gpl-3.0
tags:
- human-pose-estimation
- pose-estimation
- probabilistic
- computer-vision
datasets:
- COCO
- CropCOCO
- OCHuman
metrics:
- mAP
- Ex-mAP
📦 ProbPose: Probabilistic Human Pose Estimation
ProbPose introduces a probabilistic framework for human pose estimation, focusing on reducing false positives by predicting keypoint presence probabilities and handling out-of-image keypoints. It also introduces the new Ex-OKS metric to evaluate models on false positive predictions.
📝 Model Details
- Model type: ViT-s backbone with ProbPose head
- Input: RGB images (192x256)
- Output: Coordinates, uncertainties, quality and visibility for human keypoints
- Language(s): Not language-dependent (vision model)
- License: GPL-3.0
- Framework: MMPose
🧠 Training
- Training data: COCO Dataset
- Training script: GitHub - ProbPose_code
- Epochs: 210
- Batch size: 64
- Learning rate: 5e-5
- Hardware: 4x NVIDIA A-100
📈 Evaluation
- Metrics: mAP and Ex-mAP
- With GT bounding boxes
| Dataset | mAP | Ex-mAP |
|---|---|---|
| COCO | 76.6 | 76.4 |
| CropCOCO | 81.7 | 73.9 |
| OCHuman | 60.4 | 60.2 |
📄 Citation
If you use ProbPose in your research, please cite:
@inproceedings{probpose2025,
title={{ProbPose: A Probabilistic Approach to 2D Human Pose Estimation}},
author={Miroslav Purkrabek and Jiri Matas},
year={2025},
booktitle={Computer Vision and Pattern Recognition (CVPR)},
}
🧑💻 Authors
- Miroslav Purkrabek (personal website)
- Jiri Matas (personal website)