This repository contains a PyTorch(1.5.0) implementation of Improved Embeddings with Easy Positive Triplet Mining(WACV2020)
Update History: June 10, 2020 cleaned the code replace the learner object with directly train codes
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
Please cite our paper, if you use these functions for recall calculation.
Pytorch 1.5.0
Python 3.7
06/14/2020
Reorganize the training code.
@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}
}
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