Aggregation and Finetuning for Clothes Landmark Detection

[WIP] Code release is still in preparation, stay tuned.

1st place solution for CVPR 2020 DeepFashion2 Clothes Landmark Detection Track. This repo contains code for the keypoints detector. The object detection part is not contained.

Prerequisite

Install

• install HRNet dependencies as in HRNet/HRNet-Human-Pose-Estimation, then

  cd HRNet-Human-Pose-Estimation/lib
  make
  

• install cocoapi in switchablenorms/DeepFashion2

  git clone https://github.com/switchablenorms/DeepFashion2
  cd DeepFashion2/deepfashion2_api/PythonAPI/
  make install

Data preprocess

• download data to HRNet-Human-Pose-Estimation/lib/data/deepfashion2/

• preprocess data to following structure

  data/deepfashion2/
  	annotations/ # original data, converted from deepfashion2_to_coco.py
  		train_coco.json
  		validation_coco.json
  		test_coco.json
  	annotations_agg81kps/ # for training in aggregation
  		train_coco_agg81kps.json
  	annotations_per_category/ # for validation in finetuning
  		validation_coco_category_1.json
  		...
  		validation_coco_category_13.json
  	annotations_per_category_agg81kps/ # for training in finetuning
  		train_coco_category_1.json
  		...
  		train_coco_category_13.json
  	images/ # images, make some symlinks (or just cp) for this to be done
  		train/
  		validation/
  		test/

• COCO pretrained weights is downloaded from here to HRNet-Human-Pose-Estimation/models/pytorch/pose_coco/pose_hrnet_w48_384x288.pth

Usage

Training

• Aggregation

  python tools/train.py --cfg experiments/deepfashion2/w48_512x384_adam_lr1e-3-agg81kps.yaml

• Finetune

  # finetuning 1st category
  python tools/train.py --cfg experiments/deepfashion2/w48_512x384_adam_lr1e-3-agg81kps-category1-hflip.yaml
  # finetuning 2nd category
  python tools/train.py --cfg experiments/deepfashion2/w48_512x384_adam_lr1e-3-agg81kps-category2-hflip.yaml
  # finetuning others ...

​

Testing

• Testing on validation set (with gt det)

  CUDA_VISIBLE_DEVICES=0,1,2,3 python tools/test.py \
      --cfg experiments/deepfashion2/w48_512x384_adam_lr1e-3-agg81kps-category1-hflip.yaml \
  GPUS '(0,1,2,3)' \
      TEST.MODEL_FILE output/deepfashion2agg81kps/pose_hrnet/w48_512x384_adam_lr1e-3-agg81kps-category1-hflip/final_state.pth \
      TEST.USE_GT_BBOX True \
      TEST.IMAGE_THRE 0.01 \
      DATASET.TEST_SET validation \
      TEST.FLIP_TEST True

• Testing on validation set (with object det results /path/to/det.json)

  CUDA_VISIBLE_DEVICES=0,1,2,3 python tools/test.py \
      --cfg experiments/deepfashion2/w48_512x384_adam_lr1e-3-agg81kps-category1-hflip.yaml \
  GPUS '(0,1,2,3)' \
      TEST.MODEL_FILE output/deepfashion2agg81kps/pose_hrnet/w48_512x384_adam_lr1e-3-agg81kps-category1-hflip/final_state.pth \
      TEST.USE_GT_BBOX False \
      TEST.COCO_BBOX_FILE '/path/to/det.json' \
      TEST.IMAGE_THRE 0.01 \
      DATASET.TEST_SET validation \
      TEST.FLIP_TEST True
  
  ...

Reference

HRNet/HRNet-Human-Pose-Estimation

Please cite following if you use the code

@misc{lin2020aggregation,
    title={Aggregation and Finetuning for Clothes Landmark Detection},
    author={Tzu-Heng Lin},
    year={2020},
    eprint={2005.00419},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}

License

MIT