/RCDML_pytorch

Official PyTorch implementation of Relationship Constraint Deep Metric Learning.

Primary LanguagePythonMIT LicenseMIT

Relationship Constraint Deep Metric Learning

Official PyTorch implementation of Relationship Constraint Deep Metric Learning.

Requirements

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

python resave/train_best.py --seed 2412 \
                --src 0.01 \  # the weight of Sample Relationship Constraint
                --prc 0.05 \  # the weight of Proxy Relationship Constraint
                --model bn_inception \
                --embedding-size 512 \
                --batch-size 128 \
                --lr 1e-4 \
                --dataset cub

Evaluating Image Retrieval

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

Trained best model will be saved in the ./logs/folder_name.

# The parameters should be changed according to the model to be evaluated.
python code/evaluate.py --gpu-id 0 \
                   --batch-size 128 \
                   --model bn_inception \
                   --embedding-size 512 \
                   --dataset cub \
                   --resume /set/your/model/path/best_model.pth

Acknowledgements

Our code is modified and adapted on these great repositories: