LPAE-Net

Description

This responsitory contains the code of paper "Personalized Outfit Recommendation with Learnable Anchors - CVPR 2021"

Train the model

1. Clone this responsitory with submodules

   git clone --recurse-submodules https://github.com/lzcn/LPAE-Net.git

2. Install submodules

   • torchutils is my personal responsitory that contains utilities for PyTorch.

     cd torchutils
     python setup.py install

   • outfit-datasets is another responsitory that contains currently used fashion datasets for outfit recommendation.

     cd outfit-datasets
     python setup.py install

3. In each folder of outfit-datasets, use the scripts to prepare the dataset. I will improve the outfit-datasets so that you can test the model on different datasets that are not used in the original paper.

4. Use the run_lpae_net.py to train or test.

   • Train LPAE-Net

     ./run_lpae_net.py train \
        --cfg configs/polyvore_630_lpae_u_resnet34_nn.yaml \
        --log-dir summaries/polyvore_630_lpae_u_resnet34_nn
        --gpus 0 \
        --name train

   • Evaluate AUC

     ./run_lpae_net.py evaluate \
        --cfg configs/polyvore_630_lpae_u_resnet34_nn.yaml \
        --log-dir summaries/polyvore_630_lpae_u_resnet34_nn \
        --load-trained summaries/polyvore_630_lpae_u_resnet34_nn/checkpoints/best_model.pt \
        --gpus 0 \
        --name evalute-auc

   • Evaluate FITB

     uncomment the following line in the configuration file to evaluate the FITB using corresponding dataset

     dataset: !include "data-fitb.yaml"

     ./run_lpae_net.py fitb \
        --cfg configs/polyvore_630_lpae_u_resnet34_nn.yaml \
        --log-dir summaries/polyvore_630_lpae_u_resnet34_nn \
        --load-trained summaries/polyvore_630_lpae_u_resnet34_nn/checkpoints/best_model.pt \
        --gpus 0 \
        --name evalute-fitb

Logs

• LPAE-u (ResNet-34-nn) Polyvore-630

  • config.yaml
  • train.log

• LPAE-u (ResNet-34-nn) Polyvore-519

  • config.yaml
  • train.log

ResNet-34-nn reprensents the pretrained image features extracted from ResNet-34, i.e. the backbone is not fine-tuned.

Contact

email: zhilu@std.uestc.edu.cn