Cleaned test data list of DukeMTMC-reID, ICCV2021

Overview

Cleaned DukeMTMC-reID

Cleaned data list of DukeMTMC-reID released with our paper accepted by ICCV 2021: Learning Instance-level Spatial-Temporal Patterns for Person Re-identification

Two kinds of samples are taken into consideration:

1. The samples with wrong labels, such as:

arch

The labels of this kind of samples are corrected. The percentage of corrected identifications in training database is 1.285%, and this percentage in test database is 1.282%.

2. The samples in which the pedestrian is completely occluded, such as:

arch

This kind of samples are eliminated. The percentage of this kind of samples in training database is 0.097%, and this percentage in test database is 0.146%.

Cleaned database

The data of DukeMTMC-reID can be found here.

The list of cleaned training database is train_cleaned.txt

The query list of cleaned test database is query_cleaned.txt

The gallery list of cleaned test database is gallery_cleaned.txt

There are two elements in each line of the cleaned lists: file name and label.

arch

Dataset Licence

Please follow the LICENSE_DukeMTMC-reID. You are free to share, create and adapt the DukeMTMC-reID dataset, in the manner specified in the license.

Citation

If you find our cleaned database useful in your research, please consider to cite:

@inproceedings{ren2021learning,
  author={Ren, Min and He, Lingxiao and Liao, Xingyu and Liu, Wu and Wang, Yunlong and Tan, Tieniu},
  title={Learning Instance-level Spatial-Temporal Patterns for Person Re-identification},
  booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
  year={2021}
}
S2-BNN: Bridging the Gap Between Self-Supervised Real and 1-bit Neural Networks via Guided Distribution Calibration (CVPR 2021)

S2-BNN (Self-supervised Binary Neural Networks Using Distillation Loss) This is the official pytorch implementation of our paper: "S2-BNN: Bridging th

Zhiqiang Shen 52 Dec 24, 2022
Bayesian Deep Learning and Deep Reinforcement Learning for Object Shape Error Response and Correction of Manufacturing Systems

Bayesian Deep Learning for Manufacturing 2.0 (dlmfg) Object Shape Error Response (OSER) Digital Lifecycle Management - In Process Quality Improvement

Sumit Sinha 30 Oct 31, 2022
The mini-MusicNet dataset

mini-MusicNet A music-domain dataset for multi-label classification Music transcription is sequence-to-sequence prediction problem: given an audio per

John Thickstun 4 Nov 09, 2022
Transfer Learning library for Deep Neural Networks.

Transfer and meta-learning in Python Each folder in this repository corresponds to a method or tool for transfer/meta-learning. xfer-ml is a standalon

Amazon 245 Dec 08, 2022
OpenAi's gym environment wrapper to vectorize them with Ray

Ray Vector Environment Wrapper You would like to use Ray to vectorize your environment but you don't want to use RLLib ? You came to the right place !

Pierre TASSEL 15 Nov 10, 2022
Deep Surface Reconstruction from Point Clouds with Visibility Information

Data, code and pretrained models for the paper Deep Surface Reconstruction from Point Clouds with Visibility Information.

Raphael Sulzer 23 Jan 04, 2023
A large dataset of 100k Google Satellite and matching Map images, resembling pix2pix's Google Maps dataset.

Larger Google Sat2Map dataset This dataset extends the aerial ⟷ Maps dataset used in pix2pix (Isola et al., CVPR17). The provide script download_sat2m

34 Dec 28, 2022
A PyTorch Extension: Tools for easy mixed precision and distributed training in Pytorch

Introduction This is a Python package available on PyPI for NVIDIA-maintained utilities to streamline mixed precision and distributed training in Pyto

Artit 'Art' Wangperawong 5 Sep 29, 2021
Implementation of "Meta-rPPG: Remote Heart Rate Estimation Using a Transductive Meta-Learner"

Meta-rPPG: Remote Heart Rate Estimation Using a Transductive Meta-Learner This repository is the official implementation of Meta-rPPG: Remote Heart Ra

Eugene Lee 137 Dec 13, 2022
Extract MNIST handwritten digits dataset binary file into bmp images

MNIST-dataset-extractor Extract MNIST handwritten digits dataset binary file into bmp images More info at http://yann.lecun.com/exdb/mnist/ Dependenci

Omar Mostafa 6 May 24, 2021
cl;asification problem using classification models in supervised learning

wine-quality-predition---classification cl;asification problem using classification models in supervised learning Wine Quality Prediction Analysis - C

Vineeth Reddy Gangula 1 Jan 18, 2022
Pseudo-mask Matters in Weakly-supervised Semantic Segmentation

Pseudo-mask Matters in Weakly-supervised Semantic Segmentation By Yi Li, Zhanghui Kuang, Liyang Liu, Yimin Chen, Wayne Zhang SenseTime, Tsinghua Unive

33 Oct 14, 2022
Text-to-Image generation

Generate vivid Images for Any (Chinese) text CogView is a pretrained (4B-param) transformer for text-to-image generation in general domain. Read our p

THUDM 1.3k Dec 29, 2022
Simple-Image-Classification - Simple Image Classification Code (PyTorch)

Simple-Image-Classification Simple Image Classification Code (PyTorch) Yechan Kim This repository contains: Python3 / Pytorch code for multi-class ima

Yechan Kim 8 Oct 29, 2022
Autoencoders pretraining using clustering

Autoencoders pretraining using clustering

IITiS PAN 2 Dec 16, 2021
A Differentiable Recipe for Learning Visual Non-Prehensile Planar Manipulation

A Differentiable Recipe for Learning Visual Non-Prehensile Planar Manipulation This repository contains the source code of the paper A Differentiable

Bernardo Aceituno 2 May 05, 2022
Discovering and Achieving Goals via World Models

Discovering and Achieving Goals via World Models [Project Website] [Benchmark Code] [Video (2min)] [Oral Talk (13min)] [Paper] Russell Mendonca*1, Ole

Oleg Rybkin 71 Dec 22, 2022
DrQ-v2: Improved Data-Augmented Reinforcement Learning

DrQ-v2: Improved Data-Augmented RL Agent Method DrQ-v2 is a model-free off-policy algorithm for image-based continuous control. DrQ-v2 builds on DrQ,

Facebook Research 234 Jan 01, 2023
STBP is a way to train SNN with datasets by Backward propagation.

Spiking neural network (SNN), compared with depth neural network (DNN), has faster processing speed, lower energy consumption and more biological interpretability, which is expected to approach Stron

Ling Zhang 18 Dec 09, 2022
Adversarial Adaptation with Distillation for BERT Unsupervised Domain Adaptation

Knowledge Distillation for BERT Unsupervised Domain Adaptation Official PyTorch implementation | Paper Abstract A pre-trained language model, BERT, ha

Minho Ryu 29 Nov 30, 2022