Experiment about Deep Person Re-identification with EfficientNet-v2

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
Lecture materials for Cornell CS5785 Applied Machine Learning (Fall 2021)

Applied Machine Learning (Cornell CS5785, Fall 2021) This repo contains executable course notes and slides for the Applied ML course at Cornell and Co

Volodymyr Kuleshov 103 Dec 31, 2022
This is a computer vision based implementation of the popular childhood game 'Hand Cricket/Odd or Even' in python

Hand Cricket Table of Content Overview Installation Game rules Project Details Future scope Overview This is a computer vision based implementation of

Abhinav R Nayak 6 Jan 12, 2022
Creative Applications of Deep Learning w/ Tensorflow

Creative Applications of Deep Learning w/ Tensorflow This repository contains lecture transcripts and homework assignments as Jupyter Notebooks for th

Parag K Mital 1.5k Dec 30, 2022
Crosslingual Segmental Language Model

Crosslingual Segmental Language Model This repository contains the code from Multilingual unsupervised sequence segmentation transfers to extremely lo

C.M. Downey 1 Jun 13, 2022
Unsupervised Foreground Extraction via Deep Region Competition

Unsupervised Foreground Extraction via Deep Region Competition [Paper] [Code] The official code repository for NeurIPS 2021 paper "Unsupervised Foregr

28 Nov 06, 2022
Predicting path with preference based on user demonstration using Maximum Entropy Deep Inverse Reinforcement Learning in a continuous environment

Preference-Planning-Deep-IRL Introduction Check my portfolio post Dependencies Gym stable-baselines3 PyTorch Usage Take Demonstration python3 record.

Tianyu Li 9 Oct 26, 2022
Code for paper Novel View Synthesis via Depth-guided Skip Connections

Novel View Synthesis via Depth-guided Skip Connections Code for paper Novel View Synthesis via Depth-guided Skip Connections @InProceedings{Hou_2021_W

8 Mar 14, 2022
Official implement of "CAT: Cross Attention in Vision Transformer".

CAT: Cross Attention in Vision Transformer This is official implement of "CAT: Cross Attention in Vision Transformer". Abstract Since Transformer has

100 Dec 15, 2022
We are More than Our JOints: Predicting How 3D Bodies Move

We are More than Our JOints: Predicting How 3D Bodies Move Citation This repo contains the official implementation of our paper MOJO: @inproceedings{Z

72 Oct 20, 2022
When in Doubt: Improving Classification Performance with Alternating Normalization

When in Doubt: Improving Classification Performance with Alternating Normalization Findings of EMNLP 2021 Menglin Jia, Austin Reiter, Ser-Nam Lim, Yoa

Menglin Jia 13 Nov 06, 2022
Unsupervised Representation Learning via Neural Activation Coding

Neural Activation Coding This repository contains the code for the paper "Unsupervised Representation Learning via Neural Activation Coding" published

yookoon park 5 May 26, 2022
Implementation for the "Surface Reconstruction from 3D Line Segments" paper.

Surface Reconstruction from 3D Line Segments Surface reconstruction from 3d line segments. Langlois, P. A., Boulch, A., & Marlet, R. In 2019 Internati

85 Jan 04, 2023
This is the official implementation for the paper "(Almost) Free Incentivized Exploration from Decentralized Learning Agents" in NeurIPS 2021.

Observe then Incentivize Experiments This is the code used for the paper "(Almost) Free Incentivized Exploration from Decentralized Learning Agents",

Cong Shen Research Group 0 Mar 08, 2022
Python Blood Vessel Topology Analysis

Python Blood Vessel Topology Analysis This repository is not being updated anymore. The new version of PyVesTo is called PyVaNe and is available at ht

6 Nov 15, 2022
Code for C2-Matching (CVPR2021). Paper: Robust Reference-based Super-Resolution via C2-Matching.

C2-Matching (CVPR2021) This repository contains the implementation of the following paper: Robust Reference-based Super-Resolution via C2-Matching Yum

Yuming Jiang 151 Dec 26, 2022
Python Algorithm Interview Book Review

파이썬 알고리즘 인터뷰 책 리뷰 리뷰 IT 대기업에 들어가고 싶은 목표가 있다. 내가 꿈꿔온 회사에서 일하는 사람들의 모습을 보면 멋있다고 생각이 들고 나의 목표에 대한 열망이 강해지는 것 같다. 미래의 핵심 사업 중 하나인 SW 부분을 이끌고 발전시키는 우리나라의 I

SharkBSJ 1 Dec 14, 2021
You Only 👀 One Sequence

You Only 👀 One Sequence TL;DR: We study the transferability of the vanilla ViT pre-trained on mid-sized ImageNet-1k to the more challenging COCO obje

Hust Visual Learning Team 666 Jan 03, 2023
SlideGraph+: Whole Slide Image Level Graphs to Predict HER2 Status in Breast Cancer

SlideGraph+: Whole Slide Image Level Graphs to Predict HER2 Status in Breast Cancer A novel graph neural network (GNN) based model (termed SlideGraph+

28 Dec 24, 2022
Keras Realtime Multi-Person Pose Estimation - Keras version of Realtime Multi-Person Pose Estimation project

This repository has become incompatible with the latest and recommended version of Tensorflow 2.0 Instead of refactoring this code painfully, I create

M Faber 769 Dec 08, 2022
Visualizing Yolov5's layers using GradCam

YOLO-V5 GRADCAM I constantly desired to know to which part of an object the object-detection models pay more attention. So I searched for it, but I di

Pooya Mohammadi Kazaj 200 Jan 01, 2023