Improved Embeddings with Easy Positive Triplet Mining

This repository contains a PyTorch(1.1.0) implementation of Improved Embeddings with Easy Positive Triplet Mining(WACV2020)

Paper link: http://openaccess.thecvf.com/content_WACV_2020/papers/Xuan_Improved_Embeddings_with_Easy_Positive_Triplet_Mining_WACV_2020_paper.pdf

Prepare the training data and testing data in python dictionary format.

For example:

data_dict = {'tra' : {'class_tra_01':[image path list],
                      'class_tra_02':[image path list],
                      'class_tra_03':[image path list],
                      ....,
                      'class_tra_XX':[image path list]}
                 
             'test': {'class_test_01':[image path list],
                      'class_test_02':[image path list],
                      'class_test_03':[image path list],
                      ....,
                      'class_test_XX':[image path list]}
            }

Replace Data and data_dict in the file main.py

We only have the color nomarlization info for CUB, CAR, SOP, In-shop cloth, and PKU vehicleID data. If you use other dataset please add the color nomarlization data in the file: _code/color_lib.py

We also supply our efficient recall@K accuracy calculation functions which are located in _code/Utils.py

This function is for CAR,CUB and SOP dataset
recall(Fvec, imgLab,rank=None) 
Fvec:   Feature vectors, N by D torch.Tensor
imgLab: Image label, python list
rank:   k of recall@k, python list

This function is for In-shop Cloth dataset
recall2(Fvec_val, Fvec_gal, imgLab_val, imgLab_gal,rank=None) 
Fvec_val:     Probe feature vectors, N_val by D torch.Tensor
Fvec_gal:     Gallary feature vectors, N_gal by D torch.Tensor
imgLab_val:   Probe image label, python list
imgLab_gal:   Gallary image label, python list
rank:         k of recall@k, python list

The example of calling the function is shown in Recall.ipynb

Please cite our paper, if you use these functions for recall calculation.

Requirements

Pytorch 1.0.0

Python 3.7

Updates

Citation

@InProceedings{Xuan_2020_WACV,
author = {Xuan, Hong and Stylianou, Abby and Pless, Robert},
title = {Improved Embeddings with Easy Positive Triplet Mining},
booktitle = {The IEEE Winter Conference on Applications of Computer Vision (WACV)},
month = {March},
year = {2020}
}