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Fashionformer ECCV-2022 Video,Poster

A simple, effective and unified baseline for human fashion segmentation and recognition (ECCV 2022)

Shilin Xu*, Xiangtai Li*, Jingbo Wang, Guangliang Cheng, Yunhai Tong, Dacheng Tao.

Introduction

We present a simple, effective, unified baseline for fashion segmentation and attribute recognition. The figure below shows that the entire architecture is the Encoder-Decoder framework, like DETR.

This codebase also contains the implementation of MaskAttribute-RCNN.

Fashionformer achieve new state-of-the-art results on three fashion segmentation datasets.

Requirements

We adopt the Open-MMLab codebase and use the specific version of mmdetection and mmcv. To run this code, make sure you have mmcv and mmdet in your environment.

• Python=3.8.13, CUDA=11.1
• PyTorch=1.9.0, torchvision=0.10.0
• mmcv==1.3.18 (full version, need CUDA extension)
• mmdet==2.18.0

DataSet

Fashionpedia Dataset

Images

• Training images
• Validation and test images

Annotations

Detection: apparel object instance segmentation with localized attributes prediction:

• instances_attributes_train2020
• instances_attributes_val2020
• test_images_info2020

Global attributes prediction:

• attributes_train2020
• attributes_val2020
• test_images_info2020: same as detection task (Not used)

path/to/Fashionpedia/
├── annotations/  # annotation json files
│   ├── attributes_train2020.json
│   ├── attributes_val2020.json
│   ├── instances-attributes_train2020.json
│   ├── instances-attributes_val2020.json
└── train/
└── test/
│   ├── train2017/    # train images
│   ├── val2017/      # val images
│   └── test2017/     # test images

ModaNet

Please see the details of this link.

DeepFashion

Please use the default setting by mmdetection.

Training and Testing

Training

# for single machine 
./tools/dist_train.sh $config $num_gpu
# for multi machine with slurm
./tools/slurm_train.sh $partition $job_name $config $work_dir

Testing

# for single machine 
./tools/dist_test.sh $config $checkpoint $num_gpu --eval segm
# for multi machine with slurm
./tools/slurm_test.sh $partition $job_name $config $checkpoint --eval segm

Demo Visulization

python demo/image_demo.py $img $config $checkpoint

Trained Model

We give the config to reproduce the Fashionformer and Mask-Attributes Mask-RCNN.

Fashionpedia

Fashionformer CheckPoints one drive and baidu yun Access Code: uvlc;

Acknowledgement

We build our codebase based on K-Net and mmdetection. Much thanks for their open-sourced code. In particular, we modify the K-Net the kernel prediction head with extra attribute query prediction, which makes a two-stream query(kernel) prediction framework.

Citation

If you find this repo is useful for your research, Please consider citing our paper:

@article{xu2022fashionformer,
  title={Fashionformer: A simple, Effective and Unified Baseline for Human Fashion Segmentation and Recognition},
  author={Xu, Shilin and Li, Xiangtai and Wang, Jingbo and Cheng, Guangliang and Tong, Yunhai and Tao, Dacheng},
  journal={ECCV},
  year={2022}
}