DeepFashion Retrieval 2020

This is an updated implementation of Simple image retrieval algorithm on deep-fashion dataset with pytorch

Dependencies

• Python (Compatible with 2 and 3) (I prefer 2.7 for compatibility reasons though 3 will work just fine)
• Pytorch
• Torchvision (installed with pytorch, so don't worry)
• PIL
• cv2(only for visualizing)

Anaconda is recommended.

Get started now with preefined weights

Generating feature databases

• Feature extraction

  • Set DUMPED_MODEL in config.py as trained model
  • Run feature_extractor.py

  The feature will be saved to DATASET_BASE/all_feat.npy, DATASET_BASE/all_color_feat.npy and DATASET_BASE/all_feat.list.

• Accelerating querying by clustering

  • Run kmeans.py to train the models, default 50 clusters.

    The model will be saved as DATABASE/models/kmeans.m

Query with a picture

• Run retrieval.py img_path, for example:

  python retrieval.py img/Sheer_Pleated-Front_Blouse/img_00000005.jpg.

  Set DISTANCE_METRIC in config.py to use different metrics such as cosine, euclidean on deep feature and color feature..

output

For training on your own (unless you have a good GPU, don't even try)

Training

1. Download dataset from DeepFashion: Attribute Prediction
2. Unzip all files and set DATASET_BASE in config.py
3. Run train.py

The models will be saved to DATASET_BASE/models.

My model: Download from Google Drive

Deep Feature: ResNet50 - (Linear 1024 to 512) - (Linear 512 to 20), the 512-dim vector is regarded as images' identical features.

Loss: CrossEntropyLoss + TripletMarginLoss * Weight

Color Feature: Get ResNet50 final conv layer output(N * C * 7 * 7), then do avg_pooling on channel dim. Choose the max-N responses and extract the corresponding blocks on avg_pooling map of original image.

Training details: Freeze the conv parameters and train net until a stable accuracy and loss, then set FREEZE to False and train it again.

If you applied the DeepFashion: In-shop Clothes Retrieval, you can set ENABLE_INSHOP_DATASET to True.

Directory structure:

• DATASET_BASE
  • Anno
  • Eval
  • img
  • models(generated)
  • in_shop(optional)
    • list_bbox_inshop.txt
    • list_eval_partition.txt
    • img
  • all_feat.list(generated)
  • all_feat.npy(generated)
  • all_color_feat.npy(generated)

Environment

• Intel(R) Core(TM) i7-7700HQ CPU with 16GB RAM
• GeForce GTX 1060 Ti with CUDA 10
• Ubuntu 16.04
• Pytorch 1.1 (Stable)

Acknowledgement

(https://github.com/ihciah/deep-fashion-retrieval)Warning: Not maintained anymore.