This repo covers an reference implementation for the paper Use All The Labels: A Hierarchical Multi-Label Contrastive Learning Framework in PyTorch, using Deep Fashion In-Store as an illustrative example: Use All The Labels: A Hierarchical Multi-Label Contrastive Learning Framework.
- Pytorch 1.7.0
- tensorboard_logger 0.1.0
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This code is built upon two codebases: Supervised Contrastive Learning and MoCo.
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Train pre-trained model on Deep Fashion In-store dataset
- Perpare train-listfile, val-listfile. The format is as follows:
{ "images": [ "/deep_fashion_in_store/img/WOMEN/Dresses/id_00000002/02_1_front.jpg", "/deep_fashion_in_store/img/WOMEN/Dresses/id_00000002/02_2_side.jpg", "/deep_fashion_in_store/img/WOMEN/Dresses/id_00000002/02_4_full.jpg", "/deep_fashion_in_store/img/WOMEN/Dresses/id_00000002/02_7_additional.jpg", "/deep_fashion_in_store/img/WOMEN/Blouses_Shirts/id_00000004/03_1_front.jpg" ], "categories": [ "Dresses", "Dresses", "Dresses", "Dresses", "Blouses_Shirts" ] }
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Class map can be downloaded from class_map.json
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Repeating product ids can be downloaded from repeating_product_ids.csv
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If experiment on the model transfer ability from seen classes to unseen classes, the two classes maps can be downloaded from class_map_seen.json and class_map_unseen.json.
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To train the model on Deep Fashion In-store dataset, run
python train_deepfashion.py --data ./deepfashion/ --train-listfile ./train_listfile.json --val-listfile ./val_listfile.json --test-listfile ./test_listfile.json --class-map-file ./classmap.json --num-classes 17 --learning_rate 0.5 --temp 0.1 --ckpt /pretrained_model/ --dist-url 'tcp://localhost:10001' --multiprocessing-distributed --world-size 1 --rank 0 --cosine
- To evaluate the model, run
python eval_deepfashion.py --data ./deepfashion/ --train-listfile ./train_listfile.json --val-listfile ./val_listfile.json --class-map-file ./classmap.json --num-classes 17 --learning_rate 0.5 --temp 0.1 --ckpt /trained_model/
@inproceedings{hierarchicalContrastiveLearning,
title={Use All The Labels: A Hierarchical Multi-Label Contrastive Learning Framework},
author={Shu Zhang and Ran Xu and Caiming Xiong and Chetan Ramaiah},
year={2022},
booktitle={CVPR},
}