Code and pre-trained models for our paper, “Simple Pose: Rethinking and Improving a Bottom-up Approach for Multi-Person Pose Estimation”, accepted by AAAI-2020.

Also this repo serves as the Part B of our paper "Multi-Person Pose Estimation using Body Parts" (under review). The Part A is available at this link.

A bottom-up approach for the problem of multi-person pose estimation.

1. Training
2. Evaluation
3. Demo

• Implement the models using Pytorch in auto mixed-precision.
• Supprot training on multiple GPUs (over 90% GPU usage rate on each GPU card).
• Fast data preparing and augmentation during training.
• This project can also serve as a detailed practice to the green hand in Pytorch.

1. Install packages:

   Python=3.6, Pytorch>1.0, Nvidia Apex and other packages needed.

2. Download the COCO dataset.

3. Download the pre-trained models (default configuration: download the pretrained model snapshotted at epoch 52 provided as follow).

   Download Link: BaiduCloud

4. Change the paths in the code according to your environment.

python demo_image.py

The speed of our system is tested on the MS-COCO test-dev dataset.

• Inference speed of our 4-stage IMHN with 512 × 512 input on one 2080TI GPU: 38.5 FPS (100% GPU-Util).
• Processing speed of the keypoint assignment algorithm part that is implemented in pure Python and a single process on Intel Xeon E5-2620 CPU: 5.2 FPS (has not been well accelerated).

The corresponding code is in pure python without multiprocess for now.

python evaluate.py

Before training, prepare the training data using ''SimplePose/data/coco_masks_hdf5.py''.

Multiple GUPs are recommended to use to speed up the training process, but we support different training options.

• Most code has been provided already, you can train the model with.

  1. 'train.py': single training process on one GPU only.
  2. 'train_parallel.py': signle training process on multiple GPUs using Dataparallel.
  3. 'train_distributed.py' (recommended): multiple training processes on multiple GPUs using Distributed Training:

python -m torch.distributed.launch --nproc_per_node=4 train_distributed.py

• Realtime Multi-Person Pose Estimation verson 1
• Realtime Multi-Person Pose Estimation verson 2
• Realtime Multi-Person Pose Estimation version 3
• Associative Embedding
• NVIDIA/apex

Please kindly cite this paper in your publications if it helps your research.

@inproceedings{li2019simple,
	title={Simple Pose: Rethinking and Improving a Bottom-up Approach for Multi-Person Pose Estimation},
	author={Jia Li and Wen Su and Zengfu Wang},
	booktitle = {arXiv preprint arXiv:1911.10529},
	year={2019}
}