Rethinking of Pedestrian Attribute Recognition: A Reliable Evaluation under Zero-Shot Pedestrian Identity Setting

Overview

Rethinking of Pedestrian Attribute Recognition: A Reliable Evaluation under Zero-Shot Pedestrian Identity Setting (official Pytorch implementation)

zero-shot This paper submitted to TIP is the extension of the previous Arxiv paper.

This project aims to

  1. provide a baseline of pedestrian attribute recognition.
  2. provide two new datasets RAPzs and PETAzs following zero-shot pedestrian identity setting.
  3. provide a general training pipeline for pedestrian attribute recognition and multi-label classification task.

This project provide

  1. DDP training, which is mainly used for multi-label classifition.
  2. Training on all attributes, testing on "selected" attribute. Because the proportion of positive samples for other attributes is less than a threshold, such as 0.01.
    1. For PETA and PETAzs, 35 of the 105 attributes are selected for performance evaluation.
    2. For RAPv1, 51 of the 92 attributes are selected for performance evaluation.
    3. For RAPv2 and RAPzs, 54 and 53 of the 152 attributes are selected for performance evaluation.
    4. For PA100k, all attributes are selected for performance evaluation.
    • However, training on all attributes can not bring consistent performance improvement on various datasets.
  3. EMA model.
  4. Transformer-base model, such as swin-transformer (with a huge performance improvement) and vit.
  5. Convenient dataset info file like dataset_all.pkl

Dataset Info

  • PETA: Pedestrian Attribute Recognition At Far Distance [Paper][Project]

  • PA100K[Paper][Github]

  • RAP : A Richly Annotated Dataset for Pedestrian Attribute Recognition

  • PETAzs & RAPzs : Rethinking of Pedestrian Attribute Recognition: A Reliable Evaluation under Zero-Shot Pedestrian Identity Setting Paper [Project]

Performance

Pedestrian Attribute Recognition

Datasets Models ma Acc Prec Rec F1
PA100k resnet50 80.21 79.15 87.79 87.01 87.40
-- resnet50* 79.85 79.13 89.45 85.40 87.38
-- resnet50 + EMA 81.97 80.20 88.06 88.17 88.11
-- bninception 79.13 78.19 87.42 86.21 86.81
-- TresnetM 74.46 68.72 79.82 80.71 80.26
-- swin_s 82.19 80.35 87.85 88.51 88.18
-- vit_s 79.40 77.61 86.41 86.22 86.32
-- vit_b 81.01 79.38 87.60 87.49 87.55
PETA resnet50 83.96 78.65 87.08 85.62 86.35
PETAzs resnet50 71.43 58.69 74.41 69.82 72.04
RAPv1 resnet50 79.27 67.98 80.19 79.71 79.95
RAPv2 resnet50 78.52 66.09 77.20 80.23 78.68
RAPzs resnet50 71.76 64.83 78.75 76.60 77.66
  • The resnet* model is trained by using the weighted function proposed by Tan in AAAI2020.
  • Performance in PETAzs and RAPzs based on the first version of PETAzs and RAPzs as described in paper.
  • Experiments are conducted on the input size of (256, 192), so there may be minor differences from the results in the paper.
  • The reported performance can be achieved at the first drop of learning rate. We also take this model as the best model.
  • Pretrained models are provided now at Google Drive.

Multi-label Classification

Datasets Models mAP CP CR CF1 OP OR OF1
COCO resnet101 82.75 84.17 72.07 77.65 85.16 75.47 80.02

Pretrained Models

Dependencies

  • python 3.7
  • pytorch 1.7.0
  • torchvision 0.8.2
  • cuda 10.1

Get Started

  1. Run git clone https://github.com/valencebond/Rethinking_of_PAR.git
  2. Create a directory to dowload above datasets.
    cd Rethinking_of_PAR
    mkdir data
    
  3. Prepare datasets to have following structure:
    ${project_dir}/data
        PETA
            images/
            PETA.mat
            dataset_all.pkl
            dataset_zs_run0.pkl
        PA100k
            data/
            dataset_all.pkl
        RAP
            RAP_dataset/
            RAP_annotation/
            dataset_all.pkl
        RAP2
            RAP_dataset/
            RAP_annotation/
            dataset_zs_run0.pkl
        COCO14
            train2014/
            val2014/
            ml_anno/
                category.json
                coco14_train_anno.pkl
                coco14_val_anno.pkl
    
  4. Train baseline based on resnet50
    sh train.sh
    

Acknowledgements

Codes are based on the repository from Dangwei Li and Houjing Huang. Thanks for their released code.

Citation

If you use this method or this code in your research, please cite as:

@article{jia2021rethinking,
  title={Rethinking of Pedestrian Attribute Recognition: A Reliable Evaluation under Zero-Shot Pedestrian Identity Setting},
  author={Jia, Jian and Huang, Houjing and Chen, Xiaotang and Huang, Kaiqi},
  journal={arXiv preprint arXiv:2107.03576},
  year={2021}
}
Owner
Jian
computer vision
Jian
This repository is to support contributions for tools for the Project CodeNet dataset hosted in DAX

The goal of Project CodeNet is to provide the AI-for-Code research community with a large scale, diverse, and high quality curated dataset to drive innovation in AI techniques.

International Business Machines 1.2k Jan 04, 2023
Code to accompany our paper "Continual Learning Through Synaptic Intelligence" ICML 2017

Continual Learning Through Synaptic Intelligence This repository contains code to reproduce the key findings of our path integral approach to prevent

Ganguli Lab 82 Nov 03, 2022
This repository contains the DendroMap implementation for scalable and interactive exploration of image datasets in machine learning.

DendroMap DendroMap is an interactive tool to explore large-scale image datasets used for machine learning. A deep understanding of your data can be v

DIV Lab 33 Dec 30, 2022
Constructing interpretable quadratic accuracy predictors to serve as an objective function for an IQCQP problem that represents NAS under latency constraints and solve it with efficient algorithms.

IQNAS: Interpretable Integer Quadratic programming Neural Architecture Search Realistic use of neural networks often requires adhering to multiple con

0 Oct 24, 2021
Supercharging Imbalanced Data Learning WithCausal Representation Transfer

ECRT: Energy-based Causal Representation Transfer Code for Supercharging Imbalanced Data Learning With Energy-basedContrastive Representation Transfer

Zidi Xiu 11 May 02, 2022
Implementation of a memory efficient multi-head attention as proposed in the paper, "Self-attention Does Not Need O(n²) Memory"

Memory Efficient Attention Pytorch Implementation of a memory efficient multi-head attention as proposed in the paper, Self-attention Does Not Need O(

Phil Wang 180 Jan 05, 2023
Reliable probability face embeddings

ProbFace, arxiv This is a demo code of training and testing [ProbFace] using Tensorflow. ProbFace is a reliable Probabilistic Face Embeddging (PFE) me

Kaen Chan 34 Dec 31, 2022
Kaggleship: Kaggle Notebooks

Kaggleship: Kaggle Notebooks This repository contains my Kaggle notebooks. They are generally about data science, machine learning, and deep learning.

Erfan Sobhaei 1 Jan 25, 2022
A time series processing library

Timeseria Timeseria is a time series processing library which aims at making it easy to handle time series data and to build statistical and machine l

Stefano Alberto Russo 11 Aug 08, 2022
Weakly Supervised Posture Mining with Reverse Cross-entropy for Fine-grained Classification

Fine-grainedImageClassification Weakly Supervised Posture Mining with Reverse Cross-entropy for Fine-grained Classification We trained model here: lin

ZhenchaoTang 14 Oct 21, 2022
《Rethinking Sptil Dimensions of Vision Trnsformers》(2021)

Rethinking Spatial Dimensions of Vision Transformers Byeongho Heo, Sangdoo Yun, Dongyoon Han, Sanghyuk Chun, Junsuk Choe, Seong Joon Oh | Paper NAVER

NAVER AI 224 Dec 27, 2022
PyTorch ,ONNX and TensorRT implementation of YOLOv4

PyTorch ,ONNX and TensorRT implementation of YOLOv4

4.2k Jan 01, 2023
A PyTorch implementation of "DGC-Net: Dense Geometric Correspondence Network"

DGC-Net: Dense Geometric Correspondence Network This is a PyTorch implementation of our work "DGC-Net: Dense Geometric Correspondence Network" TL;DR A

191 Dec 16, 2022
Trash Sorter Extraordinaire is a software which efficiently detects the different types of waste in a pile of random trash through feeding it pictures or videos.

Trash-Sorter-Extraordinaire Trash Sorter Extraordinaire is a software which efficiently detects the different types of waste in a pile of random trash

Rameen Mahmood 1 Nov 07, 2021
A Moonraker plug-in for real-time compensation of frame thermal expansion

Frame Expansion Compensation A Moonraker plug-in for real-time compensation of frame thermal expansion. Installation Credit to protoloft, from whom I

58 Jan 02, 2023
This program automatically runs Python code copied in clipboard

CopyRun This program runs Python code which is copied in clipboard WARNING!! USE AT YOUR OWN RISK! NO GUARANTIES IF ANYTHING GETS BROKEN. DO NOT COPY

vertinski 4 Sep 10, 2021
Real-Time Seizure Detection using EEG: A Comprehensive Comparison of Recent Approaches under a Realistic Setting

Real-Time Seizure Detection using Electroencephalogram (EEG) This is the repository for "Real-Time Seizure Detection using EEG: A Comprehensive Compar

AITRICS 30 Dec 17, 2022
PyTorch framework for Deep Learning research and development.

Accelerated DL & RL PyTorch framework for Deep Learning research and development. It was developed with a focus on reproducibility, fast experimentati

Catalyst-Team 29 Jul 13, 2022
[CVPR 2021] Pytorch implementation of Hijack-GAN: Unintended-Use of Pretrained, Black-Box GANs

Hijack-GAN: Unintended-Use of Pretrained, Black-Box GANs In this work, we propose a framework HijackGAN, which enables non-linear latent space travers

Hui-Po Wang 46 Sep 05, 2022
(Arxiv 2021) NeRF--: Neural Radiance Fields Without Known Camera Parameters

NeRF--: Neural Radiance Fields Without Known Camera Parameters Project Page | Arxiv | Colab Notebook | Data Zirui Wang¹, Shangzhe Wu², Weidi Xie², Min

Active Vision Laboratory 411 Dec 26, 2022