wzzheng/DCML

Codes for our CVPR 2021 paper "Deep Compositional Metric Learning"

Deep Compositional Metric Learning

This repository is the official implementation of Deep Compositional Metric Learning.

The majority of this codebase is built upon research and implementations provided in Paper: https://arxiv.org/abs/2002.08473 Repo: https://github.com/Confusezius/Revisiting_Deep_Metric_Learning_PyTorch

Architecturally, our DCML framework is as follows:

Datasets

CUB-200-2011

Download from (http://www.vision.caltech.edu/visipedia/CUB-200-2011.html).

Cars196

Download from (http://ai.stanford.edu/~jkrause/cars/car_dataset.html).

Standard Online Products

Download from (http://cvgl.stanford.edu/projects/lifted_struct/)

Organize the dataset as follows:

CUB200-2011

cub200
└───images
|    └───001.Black_footed_Albatross
|           │   Black_Footed_Albatross_0001_796111
|           │   ...
|    ...

CARS196

cars196
└───images
|    └───Acura Integra Type R 2001
|           │   00128.jpg
|           │   ...
|    ...

Online Products

online_products
└───images
|    └───bicycle_final
|           │   111085122871_0.jpg
|    ...
|
└───Info_Files
|    │   bicycle.txt
|    │   ...

Requirements

• Pytorch 1.2.0+ & Faiss-Gpu
• Python 3.7
• pretrainedmodels 0.7.4
• touchvision 0.5.0

Training

Training is done by using main.py, with settings available in parameters.py. The main parameters that control our compositiors and ensembles are --compos_num and --ensemble_num. For example, we initialize the --compos_num to 4 and --ensemble_num to 4. More compositions are worth trying.

To train the DCML model with margin loss on CUB200, run this command:

python main.py --dataset cub200 --tau 55 --gamma 0.2 --gpu 0 --seed 0 --compos_num 4 --ensemble_num 4 --embed_dim 128 --bs 100
               --n_epochs 300 --samples_per_class 2 --loss margin --batch_mining distance --arch resent50_frozen_normalize

Besides, our architecture can be implemented in the diva framework (http://arxiv.org/abs/2004.13458) with this command:

python main.py --dataset cub200 --tau 55 --gamma 0.2 --gpu 0 --seed 0 --compos_num 4 --ensemble_num 4 --embed_dim 128 --bs 100
               --n_epochs 300 --samples_per_class 2 --loss margin --batch_mining distance --arch resent50_frozen_normalize
               --diva_ssl fast_moco --lr 0.000015 --evaltypes all --diva_rho_decorrelation 1500 1500 1500 --diva_features discriminative selfsimilarity shared intra
               --diva_moco_temperature 0.01 --diva_moco_n_key_batches 30 --diva_aplha_ssl 0.5 diva_alpha_shared 0.3 --diva_alpha_intra 0.3

Device

We tested our code on a linux machine with an Nvidia RTX 2080ti GPU card. We recommend using a GPU card with a memory > 11GB.

Code Reference

Revisiting_Deep_Metric_Learning_PyTorch (https://github.com/Confusezius/Revisiting_Deep_Metric_Learning_PyTorch) by Confusezius.