An official PyTorch implementation of the paper "Learning by Aligning: Visible-Infrared Person Re-identification using Cross-Modal Correspondences", ICCV 2021.

Related tags

Computer VisionLbA
Overview

PyTorch implementation of Learning by Aligning (ICCV 2021)

This is an official PyTorch implementation of the paper "Learning by Aligning: Visible-Infrared Person Re-identification using Cross-Modal Correspondences", ICCV 2021.

For more details, visit our project site or see our paper.

Requirements

  • Python 3.8
  • PyTorch 1.7.1
  • GPU memory >= 11GB

Getting started

First, clone our git repository.

git clone https://github.com/cvlab-yonsei/LbA.git
cd LbA

Docker

You can use docker pull sanghslee/ps:1.7.1-cuda11.0-cudnn8-runtime

Prepare datasets

  • SYSU-MM01: download from this link.
    • For SYSU-MM01, you need to preprocess the .jpg files into .npy files by running:
      • python utils/pre_preprocess_sysu.py --data_dir /path/to/SYSU-MM01
    • Modify the dataset directory below accordingly.
      • L63 of train.py
      • L54 of test.py

Train

  • run python train.py --method full

  • Important:

    • Performances reported during training does not reflect exact performances of your model. This is due to 1) evaluation protocols of the datasets and 2) random seed configurations.
    • Make sure you seperately run test.py to obtain correct results to be reported in your paper.

Test

  • run python test.py --method full
  • The results should be around:
dataset method mAP rank-1
SYSU-MM01 baseline 49.54 50.43
SYSU-MM01 full 54.14 55.41

Pretrained weights

  • Download [SYSU-MM01]
  • The results should be:
dataset method mAP rank-1
SYSU-MM01 full 55.22 56.31

Bibtex

@article{park2021learning,
  title={Learning by Aligning: Visible-Infrared Person Re-identification using Cross-Modal Correspondences},
  author={Park, Hyunjong and Lee, Sanghoon and Lee, Junghyup and Ham, Bumsub},
  journal={arXiv preprint arXiv:2108.07422},
  year={2021}
}

Credits

Our implementation is based on Mang Ye's code here.

Comments
  • something about run this code

    something about run this code

    thanks for your code, there is something wrong when i run you code,in this line: loss = torch.mean(comask_pos * self.criterion(feat, feat_recon_pos, feat_recon_neg)) the wrong is:RuntimeError: The size of tensor a (9) must match the size of tensor b (18) at non-singleton dimension 3 could you give me some help?

    opened by zhuchuanleiqq 12
  • When running

    When running "train. Py", there is a problem on line 132 of the "model. Py" file:

    When running "train. Py", there is a problem on line（loss = torch.mean(comask_pos * self.criterion(feat, feat_recon_pos, feat_recon_neg))） 132 of the "model. Py" file: Traceback：RuntimeError: The size of tensor a (9) must match the size of tensor b (18) at non-singleton dimension 3

    opened by redsoup 1
  • Question about the training speed

    Question about the training speed

    Thanks for your work.

    When I tried to reproduce your results with an Nvidia 2080Ti (as recommended by the paper), however, the training speed seemed very slow. It nearly took 20 minutes for each epoch on SYSU-MM01, which mismatched with the reported 8 hours training time.

    I have already used cuda for acceleration. Thus, I wonder how did this happen. Thank you.

    opened by hansonchen1996 1
  • Problems about the performance

    Problems about the performance

    I have run your source code on both SYSU and RegDB datasets, but I didn't get the performance of your paper. So I want to know how to set the hyper-parameter to get the performance of your paper?

    opened by Mrkkew 1
  • Visualization problem

    Visualization problem

    Hello， Thanks for your great work, I am wondering about the visualization part, use mask and comask matrix in SYSU-MM01 dataset. Can I get some details about the steps of your visualization method? Thank you very much.

    opened by sunset233 0
Owner
CV Lab @ Yonsei University
CV Lab @ Yonsei University
GitHub Repository
Scene text recognition

AttentionOCR for Arbitrary-Shaped Scene Text Recognition Introduction This is the ranked No.1 tensorflow based scene text spotting algorithm on ICDAR2

777 Jan 09, 2023
nofacedb/faceprocessor is a face recognition engine for NoFaceDB program complex.

faceprocessor nofacedb/faceprocessor is a face recognition engine for NoFaceDB program complex. Tech faceprocessor uses a number of open source projec

NoFaceDB 3 Sep 06, 2021
Sort By Face

Sort-By-Face This is an application with which you can either sort all the pictures by faces from a corpus of photos or retrieve all your photos from

0 Nov 29, 2021
Extracting Tables from Document Images using a Multi-stage Pipeline for Table Detection and Table Structure Recognition:

Multi-Type-TD-TSR Check it out on Source Code of our Paper: Multi-Type-TD-TSR Extracting Tables from Document Images using a Multi-stage Pipeline for

Pascal Fischer 178 Dec 27, 2022
Primary QPDF source code and documentation

QPDF QPDF is a command-line tool and C++ library that performs content-preserving transformations on PDF files. It supports linearization, encryption,

QPDF 2.2k Jan 04, 2023
EAST for ICPR MTWI 2018 Challenge II (Text detection of network images)

EAST_ICPR2018: EAST for ICPR MTWI 2018 Challenge II (Text detection of network images) Introduction This is a repository forked from argman/EAST for t

QichaoWu 49 Dec 24, 2022
Image processing in Python

scikit-image: Image processing in Python Website (including documentation): https://scikit-image.org/ Mailing list: https://mail.python.org/mailman3/l

Image Processing Toolbox for SciPy 5.2k Dec 30, 2022
An advanced 2D image manipulation with features such as edge detection and image segmentation built using OpenCV

OpenCV-ToothPaint3-Advanced-Digital-Image-Editor This application named ‘Tooth Paint’ version TP_2020.3 (64-bit) or version 3 was developed within a w

JunHong 1 Nov 05, 2021
A curated list of resources for text detection/recognition (optical character recognition ) with deep learning methods.

awesome-deep-text-detection-recognition A curated list of awesome deep learning based papers on text detection and recognition. Text Detection Papers

2.4k Jan 08, 2023
A bot that plays TFT using OCR. Keeps track of bench, board, items, and plays the user defined team comp.

NOTES: To ensure best results, make sure you are running this on a computer that has decent specs. 1920x1080 fullscreen is required in League, game mu

francis 125 Dec 30, 2022
This project is basically to draw lines with your hand, using python, opencv, mediapipe.

Paint Opencv 📷 This project is basically to draw lines with your hand, using python, opencv, mediapipe. Screenshoots 📱 Tools ⚙️ Python Opencv Mediap

Williams Ismael Bobadilla Torres 3 Nov 17, 2021
Motion detector, Full body detection, Upper body detection, Cat face detection, Smile detection, Face detection (haar cascade), Silverware detection, Face detection (lbp), and Sending email notifications

Security camera running OpenCV for object and motion detection. The camera will send email with image of any objects it detects. It also runs a server that provides web interface with live stream vid

Peace 10 Jun 30, 2021
Text to QR-CODE

QR CODE GENERATO USING PYTHON Author : RAFIK BOUDALIA. Installation Use the package manager pip to install foobar. pip install pyqrcode Usage from tki

Rafik Boudalia 2 Oct 13, 2021
Create single line SVG illustrations from your pictures

Create single line SVG illustrations from your pictures

Javier Bórquez 686 Dec 26, 2022
Image augmentation library in Python for machine learning.

Augmentor is an image augmentation library in Python for machine learning. It aims to be a standalone library that is platform and framework independe

Marcus D. Bloice 4.8k Jan 04, 2023
A simple document layout analysis using Python-OpenCV

Run the application: python main.py *Note: For first time running the application, create a folder named "output". The application is a simple documen

Roinand Aguila 109 Dec 12, 2022
Recognizing cropped text in natural images.

ASTER: Attentional Scene Text Recognizer with Flexible Rectification ASTER is an accurate scene text recognizer with flexible rectification mechanism.

Baoguang Shi 681 Jan 02, 2023
kaldi-asr/kaldi is the official location of the Kaldi project.

Kaldi Speech Recognition Toolkit To build the toolkit: see ./INSTALL. These instructions are valid for UNIX systems including various flavors of Linux

Kaldi 12.3k Jan 05, 2023
Make OpenCV camera loops less of a chore by skipping the boilerplate and getting right to the interesting stuff

camloop Forget the boilerplate from OpenCV camera loops and get to coding the interesting stuff Table of Contents Usage Install Quickstart More advanc

Gabriel Lefundes 9 Nov 12, 2021
This is the official PyTorch implementation of the paper "TransFG: A Transformer Architecture for Fine-grained Recognition" (Ju He, Jie-Neng Chen, Shuai Liu, Adam Kortylewski, Cheng Yang, Yutong Bai, Changhu Wang, Alan Yuille).

TransFG: A Transformer Architecture for Fine-grained Recognition Official PyTorch code for the paper: TransFG: A Transformer Architecture for Fine-gra

Ju He 307 Jan 03, 2023