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Mean Field Theory in Deep Metric Learning

Official implementation of ICLR 2024 paper "Mean Field Theory in Deep Metric Learning"

Installation

Install torch and torchvision compatible with your CUDA version and the following libraries:

faiss-gpu
numpy
pandas
pytorch-metric-learning
tqdm
wandb

Datasets

  1. Download four public benchmarks for deep metric learning

  2. Extract the tgz or zip file into ./data/ (Exceptionally, for Cars-196, put the files in a ./data/cars196)

Training Embedding Network

CUB-200-2011

  • Train an embedding network of Inception-BN (d=512) using MeanFieldClassWiseMultiSimilarity loss
python train.py --gpu-id 0 \
                --loss MeanFieldClassWiseMultiSimilarity \
                --model bn_inception \
                --embedding-size 512 \
                --batch-size 180 \
                --alpha 0.01 \
                --beta 80 \
                --mrg 0.8 \
                --mf-reg 0 \
                --lr 1e-4 \
                --lr-ratio 2000 \
                --dataset cub \
                --warm 1 \
                --bn-freeze 1 \
                --lr-decay-step 10 \
                --patience 10
  • Train an embedding network of ResNet-50 (d=512) using MeanFieldClassWiseMultiSimilarity loss
python train.py --gpu-id 0 \
                --loss MeanFieldClassWiseMultiSimilarity \
                --model resnet50 \
                --embedding-size 512 \
                --batch-size 120 \
                --alpha 0.01 \
                --beta 80 \
                --mrg 0.8 \
                --mf-reg 0 \
                --lr 1e-4 \
                --lr-ratio 2000 \
                --dataset cub \
                --warm 5 \
                --bn-freeze 1 \
                --lr-decay-step 5 \
                --patience 10

Cars-196

  • Train an embedding network of Inception-BN (d=512) using MeanFieldClassWiseMultiSimilarity loss
python train.py --gpu-id 0 \
                --loss MeanFieldClassWiseMultiSimilarity \
                --model bn_inception \
                --embedding-size 512 \
                --batch-size 180 \
                --alpha 0.01 \
                --beta 80 \
                --mrg 0.8 \
                --mf-reg 0 \
                --lr 1e-4 \
                --lr-ratio 2000 \
                --dataset cars \
                --warm 1 \
                --bn-freeze 1 \
                --lr-decay-step 20 \
                --patience 10
  • Train an embedding network of ResNet-50 (d=512) using MeanFieldClassWiseMultiSimilarity loss
python train.py --gpu-id 0 \
                --loss MeanFieldClassWiseMultiSimilarity \
                --model resnet50 \
                --embedding-size 512 \
                --batch-size 120 \
                --alpha 0.01 \
                --beta 80 \
                --mrg 0.8 \
                --mf-reg 0 \
                --lr 1e-4 \
                --lr-ratio 2000 \
                --dataset cars \
                --warm 5 \
                --bn-freeze 1 \
                --lr-decay-step 10 \
                --patience 10

Stanford Online Products

  • Train an embedding network of Inception-BN (d=512) using MeanFieldClassWiseMultiSimilarity loss
python train.py --gpu-id 0 \
                --loss MeanFieldClassWiseMultiSimilarity \
                --model bn_inception \
                --embedding-size 512 \
                --batch-size 180 \
                --alpha 0.01 \
                --beta 80 \
                --mrg 0.8 \
                --mf-reg 0 \
                --lr 6e-4 \
                --lr-ratio 2000 \
                --dataset SOP \
                --warm 1 \
                --bn-freeze 0 \
                --lr-decay-step 20 \
                --lr-decay-gamma 0.25 \
                --patience 10

In-Shop Clothes Retrieval

  • Train an embedding network of Inception-BN (d=512) using MeanFieldClassWiseMultiSimilarity loss
python train.py --gpu-id 0 \
                --loss MeanFieldClassWiseMultiSimilarity \
                --model bn_inception \
                --embedding-size 512 \
                --batch-size 180 \
                --alpha 0.01 \
                --beta 80 \
                --mrg 0.8 \
                --mf-reg 0 \
                --lr 6e-4 \
                --lr-ratio 2000 \
                --dataset Inshop \
                --warm 1 \
                --bn-freeze 0 \
                --lr-decay-step 20 \
                --lr-decay-gamma 0.25 \
                --patience 10

Evaluating Image Retrieval

Follow the below steps to evaluate the pretrained model or your trained model.

The trained best model will be saved in the ./logs/folder_name.

# The parameters should be changed according to the model to be evaluated.
python evaluate.py --gpu-id 0 \
                   --batch-size 120 \
                   --model bn_inception \
                   --embedding-size 512 \
                   --dataset cub \
                   --resume /set/your/model/path/best_model.pth

Acknowledgments

Our code is modified and adapted on these repositories:

Citation

If you use this method or this code in your research, please cite as:

@InProceedings{Furusawa_2024_ICLR,
  author = {Furusawa, Takuya},
  title = {Mean Field Theory for Deep Metric Learning},
  booktitle = {International Conference on Learning Representations (ICLR)},
  month = {May},
  year = {2024}
}