Skip to content

Latest commit

 

History

History
180 lines (143 loc) · 5.26 KB

DATA_PREPARATION.md

File metadata and controls

180 lines (143 loc) · 5.26 KB

Data Preparation

  1. DeepFashion - Category and Attribute Prediction Benchmark

  2. DeepFashion - In-Shop Clothes Retrieval Benchmark

  3. DeepFashion - Consumer-to-Shop Clothes Retrieval Benchmark

  4. DeepFashion - Fashion Landmark Detection Benchmark

  5. Polyvore Outfits

To use the DeepFashion dataset you need to first download it to 'data/' , then follow these steps to re-organize the dataset.

cd data/
mv Category\ and\ Attribute\ Prediction\ Benchmark Attr_Predict
mv In-shop\ Clothes\ Retrieval\ Benchmark In-shop
mv Consumer-to-shop\ Clothes\ Retrieval\ Benchmark Consumer_to_shop
mv Fashion\ Landmark\ Detection\ Benchmark/ Landmark_Detect
python prepare_attr_pred.py
python prepare_in_shop.py
python prepare_consumer_to_shop.py
python prepare_landmark_detect.py

The directory should be like this:

mmfashion
├── mmfashion
├── tools
├── configs
├── data
│   ├── Attr_Predict
│   │   ├── train.txt
│   │   ├── test.txt
│   │   ├── val.txt
│   │   ├── train_attr.txt
│   │   ├── ...
│   │   ├── Img
│   │   │   ├── img
│   │   ├── Eval
│   │   │   ├── ...
│   ├── In-shop
│   │   ├── train.txt
│   │   ├── query.txt
│   │   ├── gallery.txt
│   │   ├── train_labels.txt
│   │   │   ├── ...

Prepare an AttrDataset

The file tree should be like this:

Attr_Predict
├── Anno
│   ├── list_attr_cloth.txt
│   ├── list_attr_img.txt
│   ├── list_bbox.txt
│   ├── list_category_cloth.txt
│   ├── list_category_img.txt
│   └── list_landmarks.txt
├── Eval
│   └── list_eval_partition.txt
└── Img
    ├── XXX.jpg
    └── ...

Then run python prepare_attr_pred.py to re-organize the dataset.

Please refer to dataset/ATTR_DATASET.md for more info.

Prepare an InShopDataset

We add segmentation annotations for "Fashion Parsing and Segmentation" task. Please download the updated data. The file tree should be like this:

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

Then run python prepare_in_shop.py to re-organize the dataset.

Please refer to dataset/IN_SHOP_DATASET.md for more info.

Prepare a ConsumerToShopDataset

The file tree should be like this:

Consumer_to_shop
├── Anno
│   ├── list_attr_cloth.txt
│   ├── list_attr_items.txt
│   ├── list_attr_type.txt
│   ├── list_bbox_consumer2shop.txt
│   ├── list_item_consumer2shop.txt
│   └── list_landmarks_consumer2shop.txt
├── Eval
│   └── list_eval_partition.txt
└── Img
    ├── XXX.jpg
    └── ...

Then run python prepare_consumer_to_shop.py to re-organize the dataset.

Please refer to dataset/CONSUMER_TO_SHOP_DATASET.md for more info.

Prepare a LandmarkDetectDataset

The file tree should be like this:

Landmark_Detect
├── Anno
│   ├── list_bbox.txt
│   ├── list_joints.txt
│   └── list_landmarks.txt
├── Eval
│   └── list_eval_partition.txt
└── Img
    ├── XXX.jpg
    └── ...

Then run python prepare_landmark_detect.py to re-organize the dataset.

Please refer to dataset/LANDMARK_DETECT_DATASET.md for more info.

Prepare Polyvore-Outfit dataset

Polyvore dataset is widely used for learning fashion compatibility, containing rich multimodel information like images and descriptions of fashion items, number of likes of the outfit, etc. It is firstly collected by Maryland. Here we use a better sorted and grouped version from UIUC.

Download Polyvore and put it in the data/

The file tree should be like this:

Polyvore
├── disjoint
│   ├── compatibility_test.txt
│   ├── compatibility_train.txt
│   ├── fill_in_blank_test.json
|   └── ...
├── nondisjoint
│   └── ...
└── images
│   ├── XXX.jpg
├── categories.csv
├── polyvore_item_metadata.json
├── polyvore_outfit_titles.json