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Explicit Box Detection Unifies End-to-End Multi-Person Pose Estimation

<img src="figs/crowd scene.gif" style="height:200px" /> <img src="figs/fast_speed.gif" style="height:200px" />

This is the official pytorch implementation of our ICLR 2023 paper "Explicit Box Detection Unifies End-to-End Multi-Person Pose Estimation ".

⭐ ED-Pose

We present ED-Pose, an end-to-end framework with Explicit box Detection for multi-person Pose estimation. ED-Pose re-considers this task as two explicit box detection processes with a unified representation and regression supervision. In general, ED-Pose is conceptually simple without post-processing and dense heatmap supervision.

1. For the first time, ED-Pose, as a fully end-to-end framework with a L1 regression loss, surpasses heatmap-based Top-down methods under the same backbone by 1.2 AP on COCO.
2. ED-Pose achieves the state-of-the-art with 76.6 AP on CrowdPose without test-time augmentation.

🔥 News

• 2023/08/08: 1. We support ED-Pose on the Human-Art dataset. 2. We upload the inference script for faster virtualization.

🐟 Todo

This repo contains further modifications including:

• Integrated into detrex.

• Integrated into Huggingface Spaces 🤗 using Gradio.

🚀 Model Zoo

We have put our model checkpoints here.

Results on COCO val2017 dataset

Results on CrowdPose test dataset

Results on COCO test-dev dataset

Results on COCO test-dev dataset

Results when joint-training using Human-Art and COCO datasets

🥂 Noted that training with Human-Art on ED-Pose can lead to a performance boost on MSCOCO!

Results on Human-Art validation set

Results on COCO val2017

Note:

• Any test-time augmentations is not used for ED-Pose.
• We use the Object365 dataset to pretrain the human detection of ED-Pose under the Swin-L-5scale setting.

🚢 Environment Setup

We use the DN-Deformable-DETR as our codebase. We test our models under python=3.7.3,pytorch=1.9.0,cuda=11.1. Other versions might be available as well.

1. Clone this repo

git clone https://github.com/IDEA-Research/ED-Pose.git
cd ED-Pose

2. Install Pytorch and torchvision

Follow the instruction on https://pytorch.org/get-started/locally/.

# an example:
conda install -c pytorch pytorch torchvision

3. Install other needed packages

pip install -r requirements.txt

4. Compiling CUDA operators

cd models/edpose/ops
python setup.py build install
# unit test (should see all checking is True)
python test.py
cd ../../..

For COCO data, please download from COCO download. The coco_dir should look like this:

|-- EDPose
`-- |-- coco_dir
    `-- |-- annotations
        |   |-- person_keypoints_train2017.json
        |   `-- person_keypoints_val2017.json
        `-- images
            |-- train2017
            |   |-- 000000000009.jpg
            |   |-- 000000000025.jpg
            |   |-- 000000000030.jpg
            |   |-- ... 
            `-- val2017
                |-- 000000000139.jpg
                |-- 000000000285.jpg
                |-- 000000000632.jpg
                |-- ... 

For CrowdPose data, please download from CrowdPose download, The crowdpose_dir should look like this:

|-- ED-Pose
`-- |-- crowdpose_dir
    `-- |-- json
        |   |-- crowdpose_train.json
        |   |-- crowdpose_val.json
        |   |-- crowdpose_trainval.json (generated by util/crowdpose_concat_train_val.py)
        |   `-- crowdpose_test.json
        `-- images
            |-- 100000.jpg
            |-- 100001.jpg
            |-- 100002.jpg
            |-- 100003.jpg
            |-- 100004.jpg
            |-- 100005.jpg
            |-- ... 

🥳 Run

Training on COCO:

#For ResNet-50:
export EDPOSE_COCO_PATH=/path/to/your/cocodir
  python main.py \
 --output_dir "logs/coco_r50" \
 -c config/edpose.cfg.py \
 --options batch_size=4 epochs=60 lr_drop=55 num_body_points=17 backbone='resnet50' \
 --dataset_file="coco"

#For Swin-L:
export EDPOSE_COCO_PATH=/path/to/your/cocodir
export pretrain_model_path=/path/to/your/swin_L_384_22k
  python main.py \
 --output_dir "logs/coco_swinl" \
 -c config/edpose.cfg.py \
 --options batch_size=4 epochs=60 lr_drop=55 num_body_points=17 backbone='swin_L_384_22k' \
 --dataset_file="coco"

#For ResNet-50:
export EDPOSE_COCO_PATH=/path/to/your/cocodir
  python -m torch.distributed.launch --nproc_per_node=4  main.py \
 --output_dir "logs/coco_r50" \
 -c config/edpose.cfg.py \
 --options batch_size=4 epochs=60 lr_drop=55 num_body_points=17 backbone='resnet50' \
 --dataset_file="coco"

#For Swin-L:
export EDPOSE_COCO_PATH=/path/to/your/cocodir
export pretrain_model_path=/path/to/your/swin_L_384_22k
  python -m torch.distributed.launch --nproc_per_node=4 main.py \
 --output_dir "logs/coco_swinl" \
 -c config/edpose.cfg.py \
 --options batch_size=4 epochs=60 lr_drop=55 num_body_points=17 backbone='swin_L_384_22k' \
 --dataset_file="coco"

Training on CrowdPose:

#For ResNet-50:
export EDPOSE_CrowdPose_PATH=/path/to/your/crowdpose_dir
  python main.py \
 --output_dir "logs/crowdpose_r50" \
 -c config/edpose.cfg.py \
 --options batch_size=4 epochs=80 lr_drop=75 num_body_points=14 backbone='resnet50' \
 --dataset_file="crowdpose"

#For Swin-L:
export EDPOSE_CrowdPose_PATH=/path/to/your/crowdpose_dir
export pretrain_model_path=/path/to/your/swin_L_384_22k
  python main.py \
 --output_dir "logs/crowdpose_swinl" \
 -c config/edpose.cfg.py \
 --options batch_size=4 epochs=80 lr_drop=75 num_body_points=14 backbone='swin_L_384_22k' \
 --dataset_file="crowdpose"

#For ResNet-50:
export EDPOSE_CrowdPose_PATH=/path/to/your/crowdpose_dir
  python -m torch.distributed.launch --nproc_per_node=4  main.py \
 --output_dir "logs/crowdpose_r50" \
 -c config/edpose.cfg.py \
 --options batch_size=4 epochs=80 lr_drop=75 num_body_points=14 backbone='resnet50' \
 --dataset_file="crowdpose"

#For Swin-L:
export EDPOSE_CrowdPose_PATH=/path/to/your/crowdpose_dir
export pretrain_model_path=/path/to/your/swin_L_384_22k
  python -m torch.distributed.launch --nproc_per_node=4 main.py \
 --output_dir "logs/crowdpose_swinl" \
 -c config/edpose.cfg.py \
 --options batch_size=4 epochs=80 lr_drop=75 num_body_points=14 backbone='swin_L_384_22k' \
 --dataset_file="crowdpose"

We have put the Swin-L model pretrained on ImageNet-22k here.

Evaluation on COCO:

export EDPOSE_COCO_PATH=/path/to/your/cocodir
  python -m torch.distributed.launch --nproc_per_node=4  main.py \
 --output_dir "logs/coco_r50" \
 -c config/edpose.cfg.py \
 --options batch_size=4 epochs=60 lr_drop=55 num_body_points=17 backbone='resnet50' \
 --dataset_file="coco" \
 --pretrain_model_path "./models/edpose_r50_coco.pth" \
 --eval

export EDPOSE_COCO_PATH=/path/to/your/cocodir
export pretrain_model_path=/path/to/your/swin_L_384_22k
  python -m torch.distributed.launch --nproc_per_node=4 main.py \
 --output_dir "logs/coco_swinl" \
 -c config/edpose.cfg.py \
 --options batch_size=4 epochs=60 lr_drop=55 num_body_points=17 backbone='swin_L_384_22k' \
 --dataset_file="coco" \
 --pretrain_model_path "./models/edpose_swinl_coco.pth" \
 --eval

export EDPOSE_COCO_PATH=/path/to/your/cocodir
export pretrain_model_path=/path/to/your/swin_L_384_22k
  python -m torch.distributed.launch --nproc_per_node=4 main.py \
 --output_dir "logs/coco_swinl" \
 -c config/edpose.cfg.py \
 --options batch_size=4 epochs=60 lr_drop=55 num_body_points=17 backbone='swin_L_384_22k' \
  return_interm_indices=0,1,2,3 num_feature_levels=5 \
 --dataset_file="coco" \
 --pretrain_model_path "./models/edpose_swinl_5scale_coco.pth" \
 --eval

Evaluation on CrowdPose:

export EDPOSE_CrowdPose_PATH=/path/to/your/crowdpose_dir
  python main.py \
 --output_dir "logs/crowdpose_r50" \
 -c config/edpose.cfg.py \
 --options batch_size=4 epochs=80 lr_drop=75 num_body_points=14 backbone='resnet50' \
 --dataset_file="crowdpose"\
 --pretrain_model_path "./models/edpose_r50_crowdpose.pth" \
 --eval

export EDPOSE_CrowdPose_PATH=/path/to/your/crowdpose_dir
export pretrain_model_path=/path/to/your/swin_L_384_22k
  python main.py \
 --output_dir "logs/crowdpose_swinl" \
 -c config/edpose.cfg.py \
 --options batch_size=4 epochs=80 lr_drop=75 num_body_points=14 backbone='swin_L_384_22k' \
 --dataset_file="crowdpose" \
 --pretrain_model_path "./models/edpose_swinl_crowdpose.pth" \
 --eval

export EDPOSE_CrowdPose_PATH=/path/to/your/crowdpose_dir
export pretrain_model_path=/path/to/your/swin_L_384_22k
  python -m torch.distributed.launch --nproc_per_node=4 main.py \
 --output_dir "logs/crowdpose_swinl" \
 -c config/edpose.cfg.py \
 --options batch_size=4 epochs=80 lr_drop=75 num_body_points=14 backbone='swin_L_384_22k' \
 return_interm_indices=0,1,2,3 num_feature_levels=5 \
 -- dataset_file="crowdpose" \
 --pretrain_model_path "./models/edpose_swinl_5scale_crowdpose.pth" \
 --eval

Virtualization via COCO Keypoints Format:

export EDPOSE_COCO_PATH=/path/to/your/cocodir
export Inference_Path=/path/to/your/inference_dir
  python -m torch.distributed.launch --nproc_per_node=1  main.py \
 --output_dir "logs/coco_r50" \
 -c config/edpose.cfg.py \
 --options batch_size=1 epochs=60 lr_drop=55 num_body_points=17 backbone='resnet50' \
 --dataset_file="coco" \
 --pretrain_model_path "./models/edpose_r50_coco.pth" \
 --eval

export EDPOSE_COCO_PATH=/path/to/your/cocodir
export Inference_Path=/path/to/your/inference_dir
  python -m torch.distributed.launch --nproc_per_node=1 main.py \
 --output_dir "logs/coco_swinl" \
 -c config/edpose.cfg.py \
 --options batch_size=1 epochs=60 lr_drop=55 num_body_points=17 backbone='swin_L_384_22k' \
 --dataset_file="coco" \
 --pretrain_model_path "./models/edpose_swinl_coco.pth" \
 --eval

export EDPOSE_COCO_PATH=/path/to/your/cocodir
export Inference_Path=/path/to/your/inference_dir
  python -m torch.distributed.launch --nproc_per_node=1 main.py \
 --output_dir "logs/coco_swinl" \
 -c config/edpose.cfg.py \
 --options batch_size=1 epochs=60 lr_drop=55 num_body_points=17 backbone='swin_L_384_22k' \
  return_interm_indices=0,1,2,3 num_feature_levels=5 \
 --dataset_file="coco" \
 --pretrain_model_path "./models/edpose_swinl_5scale_coco.pth" \
 --eval

💃🏻 Cite ED-Pose

@inproceedings{
yang2023explicit,
title={Explicit Box Detection Unifies End-to-End Multi-Person Pose Estimation},
author={Jie Yang and Ailing Zeng and Shilong Liu and Feng Li and Ruimao Zhang and Lei Zhang},
booktitle={International Conference on Learning Representations},
year={2023},
url={https://openreview.net/forum?id=s4WVupnJjmX}
}