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[CVPR 2022] Official PyTorch implementation for Attributable Visual Similarity Learning

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Attributable Visual Similarity Learning

PWC PWC PWC

This repository is the official PyTorch implementation of Attributable Visual Similarity Learning (CVPR 2022). arXiv

This paper proposes an attributable visual similarity learning (AVSL) framework for a more accurate and explainable similarity measure between images. Extensive experiments on the CUB-200-2011, Cars196, and Stanford Online Products datasets demonstrate significant improvements over existing deep similarity learning methods and verify the interpretability of our framework.

Framework

AEL

Datasets

CUB-200-2011

Download from here.

Organize the dataset as follows:

- cub200
    |- train
    |   |- class0
    |   |   |- image0_1
    |   |   |- ...
    |   |- ...
    |- test
        |- class100
        |   |- image100_1
        |   |- ...
        |- ...

Cars196

Download from here.

Organize the dataset as follows:

- cars196
    |- train
    |   |- class0
    |   |   |- image0_1
    |   |   |- ...
    |   |- ...
    |- test
        |- class98
        |   |- image98_1
        |   |- ...
        |- ...

Stanford Online Products

Download from here.

Organize the dataset as follows:

- online_products
    |- images
    |   |- bicycle_final 
    |   |- chair_final
    |   |- ...
    |- Info_Files
        |- Ebay_final.txt
        |- Ebay_info.txt
        |- ...

Requirements

To install requirements:

pip install -r requirements.txt

Training

Baseline models

To train resnet50 on Cars196 with ProxyAnchor-baseline, run this command as follows:

python examples/demo.py --data_path <path-to-data> --save_path <path-to-log> --device 0 --batch_size 180 --test_batch_size 180 --setting proxy_anchor --embeddings_dim 512 --proxyanchor_margin 0.1 --proxyanchor_alpha 32 --num_classes 98 --wd 0.0001 --gamma 0.5 --step 10 --lr_trunk 0.0001 --lr_embedder 0.0001 --lr_collector 0.01 --dataset cars196 --model resnet50 --delete_old --save_name proxy-anchor-resnet50-cars196-baseline --warm_up 5 --warm_up_list embedder collector

For more baseline settings, please refer to samples_baseline.

Our models

To train resnet50 on Cars196 with ProxyAnchor-AVSL, run this command as follows:

python examples/demo.py --data_path <path-to-data> --save_path <path-to-log> --device 0 --batch_size 180 --test_batch_size 180 --setting avsl_proxyanchor --feature_dim_list 512 1024 2048 --embeddings_dim 512 --avsl_m 0.5 --topk_corr 128 --prob_gamma 10 --index_p 2 --pa_pos_margin 1.8 --pa_neg_margin 2.2 --pa_alpha 16 --final_pa_pos_margin 1.8 --final_pa_neg_margin 2.2 --final_pa_alpha 16 --num_classes 98 --use_proxy --wd 0.0001 --gamma 0.5 --step 5 --dataset cars196 --model resnet50 --splits_to_eval test --warm_up 5 --warm_up_list embedder collector --loss0_weight=1 --loss1_weight=4 --loss2_weight=4 --lr_collector=0.1 --lr_embedder=0.0002 --lr_trunk=0.0002 \
--save_name proxy-anchor-resnet50-cars196-avsl

For more AVSL settings, please refer to samples_avsl.

Device

We tested our code on a linux machine with an Nvidia RTX 3090 GPU card. We recommend using a GPU card with a memory > 16GB.

Results

Results on CUB-200-2011:

Model name Recall @ 1 Recall @ 2 Recall @ 4 Recall @ 8
baseline-PA 69.7 80.0 87.0 92.4
AVSL-PA 71.9 81.7 88.1 93.2

Results on Cars196:

Model name Recall @ 1 Recall @ 2 Recall @ 4 Recall @ 8
baseline-PA 87.7 92.9 95.8 97.9
AVSL-PA 91.5 95.0 97.0 98.4

Results on Stanford Online Products:

Model name Recall @ 1 Recall @ 10 Recall @ 100
baseline-PA 78.4 90.5 96.2
AVSL-PA 79.6 91.4 96.4

Bibtex

@article{zhang2022attributable,
  title={Attributable Visual Similarity Learning},
  author={Borui Zhang and Wenzhao Zheng and Jie Zhou and Jiwen Lu},
  journal={arXiv preprint arXiv:2203.14932},
  year={2022}
}

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[CVPR 2022] Official PyTorch implementation for Attributable Visual Similarity Learning

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