Experiment about Deep Person Re-identification with EfficientNet-v2

Related tags

Deep Learningdeep-efficient-person-reid
Overview

deep-efficient-person-reid

Experiment for an uni project with strong baseline for Person Re-identification task.

We evaluated the baseline with Resnet50 and Efficienet-v2 without using pretrained models. Also Resnet50-IBN-A and Efficientnet-v2 using pretrained on ImageNet. We used two datasets: Market-1501 and CUHK03.


Pipeline

pipeline


Implementation Details

  • Random Erasing to transform input images.
  • EfficientNet-v2 / Resnet50 / Resnet50-IBN-A as backbone.
  • Stride = 1 for last convolution layer. Embedding size for Resnet50 / Resnet50-IBN-A is 2048, while for EfficientNet-v2 is 1280. During inference, embedding features will run through a batch norm layer, as known as a bottleneck for better normalization.
  • Loss function combining 3 losses:
    1. Triplet Loss with Hard Example Mining.
    2. Classification Loss (Cross Entropy) with Label Smoothing.
    3. Centroid Loss - Center Loss for reducing the distance of embeddings to its class center. When combining it with Classification Loss, it helps preventing embeddings from collapsing.
  • The default optimizer is AMSgrad with base learning rate of 3.5e-4 and multistep learning rate scheduler, decayed at epoch 30th and epoch 55th. Besides, we also apply mixed precision in training.
  • In both datasets, pretrained models were trained for 60 epochs and non-pretrained models were trained for 100 epochs.

Source Structure

.
├── config                  # hyperparameters settings
│   └── ...                 # yaml files
├
├── datasets                # data loader
│   └── ...           
├
├── market1501              # market-1501 dataset
|
├── cuhk03_release          # cuhk03 dataset
|
├── samplers                # random samplers
│   └── ...
|
├── loggers                 # test weights and visualization results      
|   └── runs
|   
├── losses                  # loss functions
│   └── ...   
|
├── nets                    # models
│   └── bacbones            
│       └── ... 
│   
├── engine                  # training and testing procedures
│   └── ...    
|
├── metrics                 # mAP and re-ranking
│   └── ...   
|
├── utils                   # wrapper and util functions 
│   └── ...
|
├── train.py                # train code 
|
├── test.py                 # test code 
|
├── visualize.py            # visualize results

Pretrained Models (on ImageNet)

  • EfficientNet-v2: link
  • Resnet50-IBN-A: link

Notebook

  • Notebook to train, inference and visualize: Notebook

Setup


  • Install dependencies, change directory to dertorch:
pip install -r requirements.txt
cd dertorch/

  • Modify config files in /configs/. You can play with the parameters for better training, testing.

  • Training:
python train.py --config_file=name_of_config_file
Ex: python train.py --config_file=efficientnetv2_market

  • Testing: Save in /loggers/runs, for example the result from EfficientNet-v2 (Market-1501): link
python test.py --config_file=name_of_config_file
Ex: python test.py --config_file=efficientnetv2_market

  • Visualization: Save in /loggers/runs/results/, for example the result from EfficienNet-v2 (Market-1501): link
python visualize.py --config_file=name_of_config_file
Ex: python visualize.py --config_file=efficientnetv2_market

Examples

Query image 1 query1

Result image 1 result1

Query image 2 query2

Result image 2 result2


Results

  • Market-1501
Models Image Size mAP Rank-1 Rank-5 Rank-10 weights
Resnet50 (non-pretrained) 256x128 51.8 74.0 88.2 93.0 link
EfficientNet-v2 (non-pretrained) 256x128 56.5 78.5 91.1 94.4 link
Resnet50-IBN-A 256x128 77.1 90.7 97.0 98.4 link
EfficientNet-v2 256x128 69.7 87.1 95.3 97.2 link
Resnet50-IBN-A + Re-ranking 256x128 89.8 92.1 96.5 97.7 link
EfficientNet-v2 + Re-ranking 256x128 85.6 89.9 94.7 96.2 link
  • CUHK03:
Models Image Size mAP Rank-1 Rank-5 Rank-10 weights
Resnet50 (non-pretrained) ... ... ... ... ... ...
EfficientNet-v2 (non-pretrained) 256x128 10.1 10.1 21.1 29.5 link
Resnet50-IBN-A 256x128 41.2 41.8 63.1 71.2 link
EfficientNet-v2 256x128 40.6 42.9 63.1 72.5 link
Resnet50-IBN-A + Re-ranking 256x128 55.6 51.2 64.0 72.0 link
EfficientNet-v2 + Re-ranking 256x128 56.0 51.4 64.7 73.4 link

The results from EfficientNet-v2 models might be better if fine-tuning properly and longer training epochs, while here we use the best parameters for the ResNet models (on Market-1501 dataset) from this paper and only trained for 60 - 100 epochs.


Citation

@article{DBLP:journals/corr/abs-2104-13643,
  author    = {Mikolaj Wieczorek and
               Barbara Rychalska and
               Jacek Dabrowski},
  title     = {On the Unreasonable Effectiveness of Centroids in Image Retrieval},
  journal   = {CoRR},
  volume    = {abs/2104.13643},
  year      = {2021},
  url       = {https://arxiv.org/abs/2104.13643},
  archivePrefix = {arXiv},
  eprint    = {2104.13643},
  timestamp = {Tue, 04 May 2021 15:12:43 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-2104-13643.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@InProceedings{Luo_2019_CVPR_Workshops,
author = {Luo, Hao and Gu, Youzhi and Liao, Xingyu and Lai, Shenqi and Jiang, Wei},
title = {Bag of Tricks and a Strong Baseline for Deep Person Re-Identification},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2019}
}

Adapted from: michuanhaohao

Owner
lan.nguyen2k
Tensor Boy
lan.nguyen2k
GitHub Repository
Offline Multi-Agent Reinforcement Learning Implementations: Solving Overcooked Game with Data-Driven Method

Overcooked-AI We suppose to apply traditional offline reinforcement learning technique to multi-agent algorithm. In this repository, we implemented be

Baek In-Chang 14 Sep 16, 2022
ADGAN - The Implementation of paper Controllable Person Image Synthesis with Attribute-Decomposed GAN

ADGAN - The Implementation of paper Controllable Person Image Synthesis with Attribute-Decomposed GAN CVPR 2020 (Oral); Pose and Appearance Attributes Transfer;

Men Yifang 400 Dec 29, 2022
Implement A3C for Mujoco gym envs

pytorch-a3c-mujoco Disclaimer: my implementation right now is unstable (you ca refer to the learning curve below), I'm not sure if it's my problems. A

Andrew 70 Dec 12, 2022
Self-Correcting Quantum Many-Body Control using Reinforcement Learning with Tensor Networks

Self-Correcting Quantum Many-Body Control using Reinforcement Learning with Tensor Networks This repository contains the code and data for the corresp

Friederike Metz 7 Apr 23, 2022
Reference models and tools for Cloud TPUs.

Cloud TPUs This repository is a collection of reference models and tools used with Cloud TPUs. The fastest way to get started training a model on a Cl

5k Jan 05, 2023
Emblaze - Interactive Embedding Comparison

Emblaze - Interactive Embedding Comparison Emblaze is a Jupyter notebook widget for visually comparing embeddings using animated scatter plots. It bun

CMU Data Interaction Group 77 Nov 24, 2022
Semiconductor Machine learning project

Wafer Fault Detection Problem Statement: Wafer (In electronics), also called a slice or substrate, is a thin slice of semiconductor, such as a crystal

kunal suryawanshi 1 Jan 15, 2022
Source code and dataset of the paper "Contrastive Adaptive Propagation Graph Neural Networks forEfficient Graph Learning"

CAPGNN Source code and dataset of the paper "Contrastive Adaptive Propagation Graph Neural Networks forEfficient Graph Learning" Paper URL: https://ar

1 Mar 12, 2022
PyTorch implementation of DD3D: Is Pseudo-Lidar needed for Monocular 3D Object detection?

PyTorch implementation of DD3D: Is Pseudo-Lidar needed for Monocular 3D Object detection? (ICCV 2021), Dennis Park*, Rares Ambrus*, Vitor Guizilini, Jie Li, and Adrien Gaidon.

Toyota Research Institute - Machine Learning 364 Dec 27, 2022
Deep Learning Algorithms for Hedging with Frictions

Deep Learning Algorithms for Hedging with Frictions This repository contains the Forward-Backward Stochastic Differential Equation (FBSDE) solver and

Xiaofei Shi 3 Dec 22, 2022
Augmenting Physical Models with Deep Networks for Complex Dynamics Forecasting

Official code of APHYNITY Augmenting Physical Models with Deep Networks for Complex Dynamics Forecasting (ICLR 2021, Oral) Yuan Yin*, Vincent Le Guen*

Yuan Yin 24 Oct 24, 2022
An open framework for Federated Learning.

Welcome to Intel® Open Federated Learning Federated learning is a distributed machine learning approach that enables organizations to collaborate on m

Intel Corporation 397 Dec 27, 2022
Code and models for ICCV2021 paper "Robust Object Detection via Instance-Level Temporal Cycle Confusion".

Robust Object Detection via Instance-Level Temporal Cycle Confusion This repo contains the implementation of the ICCV 2021 paper, Robust Object Detect

Xin Wang 69 Oct 13, 2022
(NeurIPS '21 Spotlight) IQ-Learn: Inverse Q-Learning for Imitation

Inverse Q-Learning (IQ-Learn) Official code base for IQ-Learn: Inverse soft-Q Learning for Imitation, NeurIPS '21 Spotlight IQ-Learn is an easy-to-use

Divyansh Garg 102 Dec 20, 2022
3D ResNets for Action Recognition (CVPR 2018)

3D ResNets for Action Recognition Update (2020/4/13) We published a paper on arXiv. Hirokatsu Kataoka, Tenga Wakamiya, Kensho Hara, and Yutaka Satoh,

Kensho Hara 3.5k Jan 06, 2023
Code of the lileonardo team for the 2021 Emotion and Theme Recognition in Music task of MediaEval 2021

Emotion and Theme Recognition in Music The repository contains code for the submission of the lileonardo team to the 2021 Emotion and Theme Recognitio

Vincent Bour 8 Aug 02, 2022
LAMDA: Label Matching Deep Domain Adaptation

LAMDA: Label Matching Deep Domain Adaptation This is the implementation of the paper LAMDA: Label Matching Deep Domain Adaptation which has been accep

Tuan Nguyen 9 Sep 06, 2022
⚓ Eurybia monitor model drift over time and securize model deployment with data validation

View Demo · Documentation · Medium article 🔍 Overview Eurybia is a Python library which aims to help in : Detecting data drift and model drift Valida

MAIF 172 Dec 27, 2022
Deep learning models for classification of 15 common weeds in the southern U.S. cotton production systems.

CottonWeeds Deep learning models for classification of 15 common weeds in the southern U.S. cotton production systems. requirements pytorch torchsumma

Dong Chen 8 Jun 07, 2022
Multi-modal Content Creation Model Training Infrastructure including the FACT model (AI Choreographer) implementation.

AI Choreographer: Music Conditioned 3D Dance Generation with AIST++ [ICCV-2021]. Overview This package contains the model implementation and training

Google Research 365 Dec 30, 2022