---
license: apache-2.0
datasets:
- phiyodr/coco2017
pipeline_tag: keypoint-detection
tags:
- 2D_Pose_Estimation
- MMPOSE
- RTMO
---

# Retrainable RTMO-s Model

This repository provides a fully **retrainable** RTMO-s checkpoint for 2D human pose estimation in the MMPOSE framework.

The RTMO one-stage model family (variants T, S, M, L) was originally released by the OpenMMLab team with pre-trained weights, but those official PyTorch `.pth` checkpoints do **not** preserve all parameter keys—making fine-tuning or re-training impossible within MMPOSE (see [discussion #3076](https://github.com/open-mmlab/mmpose/discussions/3076)).  

To address this limitation, PESI has faithfully reproduced the RTMO-s training procedure on the MS COCO 2017 dataset using the exact configuration from the official MMPOSE RTMO project. Our checkpoint preserves every model key, enabling you to fine-tune on custom datasets (e.g. Body7, MPII) or continue training from this strong baseline.

- **Original implementation & configs**: [MMPOSE RTMO project](https://github.com/open-mmlab/mmpose/tree/main/projects/rtmo)  
- **Issue on missing keys**: [MMPOSE #3076](https://github.com/open-mmlab/mmpose/discussions/3076)  

## Key Features

- **One-stage RTMO-s architecture** as described in the MMPOSE RTMO project  
- **All keys retained** in the PyTorch checkpoint for full retrainability (unlike the official weights)  

## References

1. OpenMMLab MMPOSE RTMO project: “RTMO: Real-time One-Stage Multi-person Pose Estimation”  
2. Issue discussion on missing keys in official RTMO checkpoints