New approach for better retrieval performance with single model: Improved Embeddings with Easy Positive Triplet Mining(WACV2020)
Paper link: https://arxiv.org/abs/1904.04370
This repository contains the PyTorch(1.5.0) implementation of Deep Randomized Ensembles for Metric Learning(ECCV2018)
Paper link: https://arxiv.org/abs/1808.04469 or https://www2.seas.gwu.edu/~pless/papers/DREML_ECCV2018.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 have the color nomarlization info for CUB, CAR, SOP, CIFAR100, In-shop cloth and PKU vehicleID data. If you want to use other dataset please add the color nomarlization value in _code/color_lib.py
We also provide efficient recall@K accuracy calculation functions in _code/Utils.py
Function 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
Function 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.
Pytorch 1.5.0
Python >3.5
06/12/2020/: Upgrade to PyTorch 1.5.0 version
05/01/2019/:
Upgrade to PyTorch 1.0.0 version
Simplified the codes structure
Fix the bug in Recall.ipynb
Add recall functions for CAR, CUB, SOP and In-shop cloth dataset
@InProceedings{Xuan_2018_ECCV,
author = {Xuan, Hong and Souvenir, Richard and Pless, Robert},
title = {Deep Randomized Ensembles for Metric Learning},
booktitle = {The European Conference on Computer Vision (ECCV)},
month = {September},
year = {2018}
}