chenjunyu 7702eed03c | 9 months ago | |
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.. | ||
README.md | 9 months ago | |
mask_rcnn_r50_fpn_15e_deepfashion.py | 9 months ago |
MMFashion(https://github.com/open-mmlab/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.
Backbone | Model type | Dataset | bbox detection Average Precision | segmentation Average Precision | Download (Google) |
---|---|---|---|---|---|
ResNet50 | Mask RCNN | DeepFashion-In-shop | 0.599 | 0.584 | model | log |
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