[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
GitHub Repository
This package implements THOR: Transformer with Stochastic Experts.

THOR: Transformer with Stochastic Experts This PyTorch package implements Taming Sparsely Activated Transformer with Stochastic Experts. Installation

Microsoft 45 Nov 22, 2022
Pytorch Code for "Medical Transformer: Gated Axial-Attention for Medical Image Segmentation"

Medical-Transformer Pytorch Code for the paper "Medical Transformer: Gated Axial-Attention for Medical Image Segmentation" About this repo: This repo

Jeya Maria Jose 615 Dec 25, 2022
Extreme Dynamic Classifier Chains - XGBoost for Multi-label Classification

Extreme Dynamic Classifier Chains Classifier chains is a key technique in multi-label classification, sinceit allows to consider label dependencies ef

6 Oct 08, 2022
Implement of homography net by pytorch

HomographyNet Implement of homography net by pytorch Brief Introduction This project is based on the work Homography-Net: @article{detone2016deep, t

ronghao_CN 4 May 19, 2022
Tensorflow implementation of the paper "HumanGPS: Geodesic PreServing Feature for Dense Human Correspondences", CVPR 2021.

HumanGPS: Geodesic PreServing Feature for Dense Human Correspondences Tensorflow implementation of the paper "HumanGPS: Geodesic PreServing Feature fo

Google Interns 50 Dec 21, 2022
Codes for realizing theories learned from Data Mining, Machine Learning, Deep Learning without using the present Python packages.

Codes-for-Algorithms Codes for realizing theories learned from Data Mining, Machine Learning, Deep Learning without using the present Python packages.

Tracy (Shengmin) Tao 1 Apr 12, 2022
Neural network-based build time estimation for additive manufacturing

Neural network-based build time estimation for additive manufacturing Oh, Y., Sharp, M., Sprock, T., & Kwon, S. (2021). Neural network-based build tim

Yosep 1 Nov 15, 2021
Resilient projection-based consensus actor-critic (RPBCAC) algorithm

Resilient projection-based consensus actor-critic (RPBCAC) algorithm We implement the RPBCAC algorithm with nonlinear approximation from [1] and focus

Martin Figura 5 Jul 12, 2022
Lenia - Mathematical Life Forms

For full version list, see Timeline in Lenia portal [2020-10-13] Update Python version with multi-kernel and multi-channel extensions (v3.4 LeniaNDK.p

Bert Chan 3.1k Dec 28, 2022
A deep-learning pipeline for segmentation of ambiguous microscopic images.

Welcome to Official repository of deepflash2 - a deep-learning pipeline for segmentation of ambiguous microscopic images. Quick Start in 30 seconds se

Matthias Griebel 39 Dec 19, 2022
Implementation supporting the ICCV 2017 paper "GANs for Biological Image Synthesis"

GANs for Biological Image Synthesis This codes implements the ICCV-2017 paper "GANs for Biological Image Synthesis". The paper and its supplementary m

Anton Osokin 95 Nov 25, 2022
3D Pose Estimation for Vehicles

3D Pose Estimation for Vehicles Introduction This work generates 4 key-points and 2 key-edges from vertices and edges of vehicles as ground truth. The

Jingyi Wang 1 Nov 01, 2021
Reproducing Results from A Hybrid Approach to Targeting Social Assistance

title author date output Reproducing Results from A Hybrid Approach to Targeting Social Assistance Lendie Follett and Heath Henderson 12/28/2021 html_

Lendie Follett 0 Jan 06, 2022
This Deep Learning Model Predicts that from which disease you are suffering.

Deep-Learning-Project This Deep Learning Model Predicts that from which disease you are suffering. This Project Covers the Topics of Deep Learning Int

Jai Viral Doshi 0 Jan 20, 2022
Everything's Talkin': Pareidolia Face Reenactment (CVPR2021)

Everything's Talkin': Pareidolia Face Reenactment (CVPR2021) Linsen Song, Wayne Wu, Chaoyou Fu, Chen Qian, Chen Change Loy, and Ran He [Paper], [Video

71 Dec 21, 2022
Codes for NeurIPS 2021 paper "Adversarial Neuron Pruning Purifies Backdoored Deep Models"

Adversarial Neuron Pruning Purifies Backdoored Deep Models Code for NeurIPS 2021 "Adversarial Neuron Pruning Purifies Backdoored Deep Models" by Dongx

Dongxian Wu 31 Dec 11, 2022
To SMOTE, or not to SMOTE?

To SMOTE, or not to SMOTE? This package includes the code required to repeat the experiments in the paper and to analyze the results. To SMOTE, or not

Amazon Web Services 1 Jan 03, 2022
PyTorch Implementation for AAAI'21 "Do Response Selection Models Really Know What's Next? Utterance Manipulation Strategies for Multi-turn Response Selection"

UMS for Multi-turn Response Selection Implements the model described in the following paper Do Response Selection Models Really Know What's Next? Utte

Taesun Whang 47 Nov 22, 2022
Physics-informed Neural Operator for Learning Partial Differential Equation

PINO Physics-informed Neural Operator for Learning Partial Differential Equation Abstract: Machine learning methods have recently shown promise in sol

107 Jan 02, 2023
Implementation of light baking system for ray tracing based on Activision's UberBake

Vulkan Light Bakary MSU Graphics Group Student's Diploma Project Treefonov Andrey [GitHub] [LinkedIn] Project Goal The goal of the project is to imple

Andrey Treefonov 7 Dec 27, 2022