[DATASET]
MMFashion develops "fashion parsing and segmentation" module based on the dataset DeepFashion-Inshop. Its annotation follows COCO style. To use it, you need to first download the data. Note that we only use "img_highres" in this task. The file tree should be like this:
mmdetection
├── mmdet
├── tools
├── configs
├── data
│ ├── DeepFashion
│ │ ├── In-shop
│ │ ├── Anno
│ │ │ ├── segmentation
│ │ │ | ├── DeepFashion_segmentation_train.json
│ │ │ | ├── DeepFashion_segmentation_query.json
│ │ │ | ├── DeepFashion_segmentation_gallery.json
│ │ │ ├── list_bbox_inshop.txt
│ │ │ ├── list_description_inshop.json
│ │ │ ├── list_item_inshop.txt
│ │ │ └── list_landmarks_inshop.txt
│ │ ├── Eval
│ │ │ └── list_eval_partition.txt
│ │ ├── Img
│ │ │ ├── img
│ │ │ │ ├──XXX.jpg
│ │ │ ├── img_highres
│ │ │ └── ├──XXX.jpg
After that you can train the Mask RCNN r50 on DeepFashion-In-shop dataset by launching training with the mask_rcnn_r50_fpn_1x.py config
or creating your own config file.
@inproceedings{liuLQWTcvpr16DeepFashion,
author = {Liu, Ziwei and Luo, Ping and Qiu, Shi and Wang, Xiaogang and Tang, Xiaoou},
title = {DeepFashion: Powering Robust Clothes Recognition and Retrieval with Rich Annotations},
booktitle = {Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2016}
}
| Backbone | Model type | Dataset | bbox detection Average Precision | segmentation Average Precision | Config | Download (Google) |
|---|---|---|---|---|---|---|
| ResNet50 | Mask RCNN | DeepFashion-In-shop | 0.599 | 0.584 | config | model | log |