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}
}
Implementation of CVPR 2021 paper "Spatially-invariant Style-codes Controlled Makeup Transfer"

SCGAN Implementation of CVPR 2021 paper "Spatially-invariant Style-codes Controlled Makeup Transfer" Prepare The pre-trained model is avaiable at http

118 Dec 12, 2022
Tracking Pipeline helps you to solve the tracking problem more easily

Tracking_Pipeline Tracking_Pipeline helps you to solve the tracking problem more easily I integrate detection algorithms like: Yolov5, Yolov4, YoloX,

VNOpenAI 32 Dec 21, 2022
This repo includes the supplementary of our paper "CEMENT: Incomplete Multi-View Weak-Label Learning with Long-Tailed Labels"

Supplementary Materials for CEMENT: Incomplete Multi-View Weak-Label Learning with Long-Tailed Labels This repository includes all supplementary mater

Zhiwei Li 0 Jan 05, 2022
Code for "LoFTR: Detector-Free Local Feature Matching with Transformers", CVPR 2021

LoFTR: Detector-Free Local Feature Matching with Transformers Project Page | Paper LoFTR: Detector-Free Local Feature Matching with Transformers Jiami

ZJU3DV 1.4k Jan 04, 2023
A toy compiler that can convert Python scripts to pickle bytecode 🥒

Pickora 🐰 A small compiler that can convert Python scripts to pickle bytecode. Requirements Python 3.8+ No third-party modules are required. Usage us

ꌗᖘ꒒ꀤ꓄꒒ꀤꈤꍟ 68 Jan 04, 2023
一个多模态内容理解算法框架,其中包含数据处理、预训练模型、常见模型以及模型加速等模块。

Overview 架构设计 插件介绍 安装使用 框架简介 方便使用,支持多模态,多任务的统一训练框架 能力列表: bert + 分类任务 自定义任务训练(插件注册) 框架设计 框架采用分层的思想组织模型训练流程。 DATA 层负责读取用户数据,根据 field 管理数据。 Parser 层负责转换原

Tencent 265 Dec 22, 2022
基于DouZero定制AI实战欢乐斗地主

DouZero_For_Happy_DouDiZhu: 将DouZero用于欢乐斗地主实战 本项目基于DouZero 环境配置请移步项目DouZero 模型默认为WP,更换模型请修改start.py中的模型路径 运行main.py即可 SL (baselines/sl/): 基于人类数据进行深度学习

1.5k Jan 08, 2023
Code of U2Fusion: a unified unsupervised image fusion network for multiple image fusion tasks, including multi-modal, multi-exposure and multi-focus image fusion.

U2Fusion Code of U2Fusion: a unified unsupervised image fusion network for multiple image fusion tasks, including multi-modal (VIS-IR, medical), multi

Han Xu 129 Dec 11, 2022
CLIP: Connecting Text and Image (Learning Transferable Visual Models From Natural Language Supervision)

CLIP (Contrastive Language–Image Pre-training) Experiments (Evaluation) Model Dataset Acc (%) ViT-B/32 (Paper) CIFAR100 65.1 ViT-B/32 (Our) CIFAR100 6

Myeongjun Kim 52 Jan 07, 2023
An efficient PyTorch implementation of the evaluation metrics in recommender systems.

recsys_metrics An efficient PyTorch implementation of the evaluation metrics in recommender systems. Overview • Installation • How to use • Benchmark

Xingdong Zuo 12 Dec 02, 2022
Project dự đoán giá cổ phiếu bằng thuật toán LSTM gồm: code train và code demo

Web predicts stock prices using Long - Short Term Memory algorithm Give me some start please!!! User interface image: Choose: DayBegin, DayEnd, Stock

Vo Thuong Truong Nhon 8 Nov 11, 2022
A Planar RGB-D SLAM which utilizes Manhattan World structure to provide optimal camera pose trajectory while also providing a sparse reconstruction containing points, lines and planes, and a dense surfel-based reconstruction.

ManhattanSLAM Authors: Raza Yunus, Yanyan Li and Federico Tombari ManhattanSLAM is a real-time SLAM library for RGB-D cameras that computes the camera

117 Dec 28, 2022
[SDM 2022] Towards Similarity-Aware Time-Series Classification

SimTSC This is the PyTorch implementation of SDM2022 paper Towards Similarity-Aware Time-Series Classification. We propose Similarity-Aware Time-Serie

Daochen Zha 49 Dec 27, 2022
End-to-End Dense Video Captioning with Parallel Decoding (ICCV 2021)

PDVC Official implementation for End-to-End Dense Video Captioning with Parallel Decoding (ICCV 2021) [paper] [valse论文速递(Chinese)] This repo supports:

Teng Wang 118 Dec 16, 2022
Planner_backend - Academic planner application designed for students and counselors.

Planner (backend) Academic planner application designed for students and advisors.

2 Dec 31, 2021
Explaining neural decisions contrastively to alternative decisions.

Contrastive Explanations for Model Interpretability This is the repository for the paper "Contrastive Explanations for Model Interpretability", about

AI2 16 Oct 16, 2022
Customizable RecSys Simulator for OpenAI Gym

gym-recsys: Customizable RecSys Simulator for OpenAI Gym Installation | How to use | Examples | Citation This package describes an OpenAI Gym interfac

Xingdong Zuo 14 Dec 08, 2022
Level Based Customer Segmentation

level_based_customer_segmentation Level Based Customer Segmentation Persona Veri Seti kullanılarak müşteri segmentasyonu yapılmıştır. KOLONLAR : PRICE

Buse Yıldırım 6 Dec 21, 2021
Code for CVPR2019 Towards Natural and Accurate Future Motion Prediction of Humans and Animals

Motion prediction with Hierarchical Motion Recurrent Network Introduction This work concerns motion prediction of articulate objects such as human, fi

Shuang Wu 85 Dec 11, 2022
Rainbow DQN implementation that outperforms the paper's results on 40% of games using 20x less data 🌈

Rainbow 🌈 An implementation of Rainbow DQN which outperforms the paper's (Hessel et al. 2017) results on 40% of tested games while using 20x less dat

Dominik Schmidt 31 Dec 21, 2022