[ICCV 2021] Official PyTorch implementation for Deep Relational Metric Learning.

Related tags

Deep LearningDRML
Overview

Deep Relational Metric Learning

This repository is the official PyTorch implementation of Deep Relational Metric Learning.

Framework

AEL

DRML

Datasets

CUB-200-2011

Download from here.

Organize the dataset as follows:

- cub200
    |- train
    |   |- class0
    |   |   |- image0_1
    |   |   |- ...
    |   |- ...
    |- test
        |- class100
        |   |- image100_1
        |   |- ...
        |- ...

Cars196

Download from here.

Organize the dataset as follows:

- cars196
    |- train
    |   |- class0
    |   |   |- image0_1
    |   |   |- ...
    |   |- ...
    |- test
        |- class98
        |   |- image98_1
        |   |- ...
        |- ...

Requirements

To install requirements:

pip install -r requirements.txt

Training

Baseline models

To train the baseline model with the ProxyAnchor loss on CUB200, run this command:

CUDA_VISIBLE_DEVICES=0 python examples/train/main.py \
--save_name <experiment-name> \
--data_path <path-of-data> \
--phase train \
--device 0 \
--setting proxy_baseline \
--dataset cub200 \
--num_classes 100 \
--batch_size 120 \
--delete_old

To train the baseline model with the ProxyAnchor loss on Cars196, run this command:

CUDA_VISIBLE_DEVICES=0 python examples/train/main.py \
--save_name <experiment-name> \
--data_path <path-of-data> \
--phase train \
--device 0 \
--setting proxy_baseline \
--dataset cars196 \
--num_classes 98 \
--batch_size 120 \
--delete_old

DRML models

To train the proposed DRML model using the ProxyAnchor loss on CUB200 in the paper, run this command:

CUDA_VISIBLE_DEVICES=0 python examples/train/main.py \
--save_name <experiment-name> \
--data_path <path-of-data> \
--phase train \
--device 0 \
--setting proxy \
--dataset cub200 \
--num_classes 100 \
--batch_size 120 \
--delete_old

To train the proposed DRML model using the ProxyAnchor loss on Cars196 in the paper, run this command:

CUDA_VISIBLE_DEVICES=0 python examples/train/main.py \
--save_name <experiment-name> \
--data_path <path-of-data> \
--phase train \
--device 0 \
--setting proxy \
--dataset cars196 \
--num_classes 98 \
--batch_size 120 \
--delete_old

Device

We tested our code on a linux machine with an Nvidia RTX 3090 GPU card. We recommend using a GPU card with a memory > 8GB (BN-Inception + batch-size of 120 ).

Results

The baseline models achieve the following performances:

Model name Recall @ 1 Recall @ 2 Recall @ 4 Recall @ 8 NMI
cub200-ProxyAnchor-baseline 67.3 77.7 85.7 91.4 68.7
cars196-ProxyAnchor-baseline 84.4 90.7 94.3 96.8 69.7

Our models achieve the following performances:

Model name Recall @ 1 Recall @ 2 Recall @ 4 Recall @ 8 NMI
cub200-ProxyAnchor-ours 68.7 78.6 86.3 91.6 69.3
cars196-ProxyAnchor-ours 86.9 92.1 95.2 97.4 72.1

COMING SOON

  • We will upload the code for cross-validation setting soon.
  • We will update the optimal hyper-parameters of the experiments soon.
Owner
Borui Zhang
I am a first year Ph.D student in the Department of Automation at THU. My research direction is computer vision.
Borui Zhang
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