DJW cdece0b32a 第一次提交 | il y a 9 mois | |
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configs | il y a 9 mois | |
datasets | il y a 9 mois | |
models | il y a 9 mois | |
README.md | il y a 9 mois | |
demo | il y a 9 mois | |
tools | il y a 9 mois |
This project implements a YOLOX-based human pose estimator, utilizing the approach outlined in YOLO-Pose: Enhancing YOLO for Multi Person Pose Estimation Using Object Keypoint Similarity Loss (CVPRW 2022). This pose estimator is lightweight and quick, making it well-suited for crowded scenes.
All the commands below rely on the correct configuration of PYTHONPATH
, which should point to the project's directory so that Python can locate the module files. In yolox-pose/
root directory, run the following line to add the current directory to PYTHONPATH
:
export PYTHONPATH=`pwd`:$PYTHONPATH
Users can apply YOLOX-Pose models to estimate human poses using the inferencer found in the MMPose core package. Use the command below:
python demo/inferencer_demo.py $INPUTS \
--pose2d $CONFIG --pose2d-weights $CHECKPOINT --scope mmyolo \
[--show] [--vis-out-dir $VIS_OUT_DIR] [--pred-out-dir $PRED_OUT_DIR]
For more information on using the inferencer, please see this document.
Here's an example code:
python demo/inferencer_demo.py ../../tests/data/coco/000000000785.jpg \
--pose2d configs/yolox-pose_s_8xb32-300e_coco.py \
--pose2d-weights https://download.openmmlab.com/mmpose/v1/projects/yolox-pose/yolox-pose_s_8xb32-300e_coco-9f5e3924_20230321.pth \
--scope mmyolo --vis-out-dir vis_results
This will create an output image vis_results/000000000785.jpg
, which appears like:
Prepare the COCO dataset according to the instruction.
To train with multiple GPUs:
bash tools/dist_train.sh $CONFIG 8 --amp
To train with slurm:
bash tools/slurm_train.sh $PARTITION $JOBNAME $CONFIG $WORKDIR --amp
To test with single GPU:
python tools/test.py $CONFIG $CHECKPOINT
To test with multiple GPUs:
bash tools/dist_test.sh $CONFIG $CHECKPOINT 8
To test with multiple GPUs by slurm:
bash tools/slurm_test.sh $PARTITION $JOBNAME $CONFIG $CHECKPOINT
Results on COCO val2017
Model | Input Size | AP | AP50 | AP75 | AR | AR50 | Download |
---|---|---|---|---|---|---|---|
YOLOX-tiny-Pose | 416 | 0.518 | 0.799 | 0.545 | 0.566 | 0.841 | model | log |
YOLOX-s-Pose | 640 | 0.632 | 0.875 | 0.692 | 0.676 | 0.907 | model | log |
YOLOX-m-Pose | 640 | 0.685 | 0.897 | 0.753 | 0.727 | 0.925 | model | log |
YOLOX-l-Pose | 640 | 0.706 | 0.907 | 0.775 | 0.747 | 0.934 | model | log |
We have only trained models with an input size of 640, as we couldn't replicate the performance enhancement mentioned in the paper when increasing the input size from 640 to 960. We warmly welcome any contributions if you can successfully reproduce the results from the paper!
If this project benefits your work, please kindly consider citing the original paper:
@inproceedings{maji2022yolo,
title={YOLO-Pose: Enhancing YOLO for Multi Person Pose Estimation Using Object Keypoint Similarity Loss},
author={Maji, Debapriya and Nagori, Soyeb and Mathew, Manu and Poddar, Deepak},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={2637--2646},
year={2022}
}
Additionally, please cite our work as well:
@misc{mmpose2020,
title={OpenMMLab Pose Estimation Toolbox and Benchmark},
author={MMPose Contributors},
howpublished = {\url{https://github.com/open-mmlab/mmpose}},
year={2020}
}