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https://github.com/codingonion/awesome-object-detection-datasets

A collection of some awesome public object detection and recognition datasets.
https://github.com/codingonion/awesome-object-detection-datasets
aerial-imagery autonomous-driving awesome-list chatgpt coco dataset datasets infrared large-language-models llm object-detection object-recognition opendata remote-sensing sar thermal yolo yolov10 yolov5 yolov8
Last synced: 3 months ago
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A collection of some awesome public object detection and recognition datasets.
Host: GitHub
URL: https://github.com/codingonion/awesome-object-detection-datasets
Owner: codingonion
Created: 2022-08-06T00:23:11.000Z (about 2 years ago)
Default Branch: main
Last Pushed: 2024-07-07T00:39:55.000Z (4 months ago)
Last Synced: 2024-07-29T21:12:02.960Z (3 months ago)
Topics: aerial-imagery, autonomous-driving, awesome-list, chatgpt, coco, dataset, datasets, infrared, large-language-models, llm, object-detection, object-recognition, opendata, remote-sensing, sar, thermal, yolo, yolov10, yolov5, yolov8
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Watchers: 3
Forks: 7
Open Issues: 0
Metadata Files:
- Readme: README.md
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README

        # Awesome-Object-Detection-Datasets

[![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/sindresorhus/awesome)

🔥🔥🔥 This repository lists some awesome public object detection and recognition datasets.

## Contents

- [Awesome-Object-Detection-Datasets](#awesome-object-detection-datasets)

  - [Summary](#summary)

    - [Awesome List](#awesome-list)

    - [Datasets Share Platform](#datasets-share-platform)

    - [Tools](#tools)

      - [Data Annotation](#data-annotation)

      - [Data Augmentation](#data-augmentation)

      - [Data Management](#data-management)

  - [General Detection and Recognition Datasets](#general-detection-and-recognition-datasets)

    - [Object Detection Datasets](#object-detection-datasets)

    - [Objecgt Recognition Datasets](#objecgt-recognition-datasets)

  - [Autonomous Driving Datasets](#autonomous-driving-datasets)

    - [Diverse Autonomous Driving Datasets](#diverse-autonomous-driving-datasets)

    - [Traffic Sign Detection Datasets](#traffic-sign-detection-datasets)

    - [License Plate Detection and Recognition Datasets](#license-plate-detection-and-recognition-datasets)

  - [Adverse Weather Datasets](#adverse-weather-datasets)

  - [Person Detection Datasets](#person-detection-datasets)

  - [Anti-UAV Datasets](#anti-uav-datasets)

  - [Optical Aerial Imagery Datasets](#optical-aerial-imagery-datasets)

  - [Low-light Image Datasets](#low-light-image-datasets)

  - [Infrared Image Datasets](#infrared-image-datasets)

  - [SAR Image Datasets](#sar-image-datasets)

  - [Multispectral Image Datasets](#multispectral-image-datasets)

  - [3D Object Detection Datasets](#3d-object-detection-datasets)

  - [Vehicle-to-Everything Field Datasets](#vehicle-to-everything-field-datasets)

  - [Super-Resolution Field Datasets](#super-resolution-field-datasets)

  - [Face Detection and Recognition Datasets](#general-detection-and-recognition-datasets)

    - [Face Detection Datasets](#face-detection-datasets)

    - [Face Recognition Datasets](#face-recognition-datasets)

  - [Blogs](#blogs)

## Summary

  - ### Awesome List

    - [wenhwu/awesome-remote-sensing-change-detection](https://github.com/wenhwu/awesome-remote-sensing-change-detection)  : List of datasets, codes, and contests related to remote sensing change detection.

    - [ZHOUYI1023/awesome-radar-perception](https://github.com/ZHOUYI1023/awesome-radar-perception)  : A curated list of radar datasets, detection, tracking and fusion.

    - [lartpang/awesome-segmentation-saliency-dataset](https://github.com/lartpang/awesome-segmentation-saliency-dataset)  : A collection of some datasets for segmentation / saliency detection. Welcome to PR...😄

    - [TianhaoFu/Awesome-3D-Object-Detection](https://github.com/TianhaoFu/Awesome-3D-Object-Detection)  : Papers, code and datasets about deep learning for 3D Object Detection.

    - [xahidbuffon/Awesome_Underwater_Datasets](https://github.com/xahidbuffon/Awesome_Underwater_Datasets)  : Pointers to large-scale underwater datasets and relevant resources.

    - [M-3LAB/awesome-industrial-anomaly-detection](https://github.com/M-3LAB/awesome-industrial-anomaly-detection)  : Paper list and datasets for industrial image anomaly detection.

    - [ZhangXiwuu/Awesome_visual_place_recognition_datasets](https://github.com/ZhangXiwuu/Awesome_visual_place_recognition_datasets)  : A curated list of Visual Place Recognition (VPR)/ loop closure detection (LCD) datasets.

    - [ari-dasci/OD-WeaponDetection](https://github.com/ari-dasci/OD-WeaponDetection)  : Datasets for weapon detection based on image classification and object detection tasks.

    - [DLLXW/objectDetectionDatasets](https://github.com/DLLXW/objectDetectionDatasets)  : 目标检测数据集制作:VOC,COCO,YOLO等常用数据集格式的制作和互相转换脚本。

    - [codingonion/awesome-object-detection-and-recognition-datasets](https://github.com/codingonion/awesome-object-detection-and-recognition-datasets)  : A collection of some awesome public object detection and recognition datasets.

  - ### Datasets Share Platform

    - [OpenDataLab](https://opendatalab.org.cn/) : OpenDataLab 是上海人工智能实验室的大模型数据基座团队打造的数据开放平台，现已成为中国大模型语料数据联盟开源数据服务指定平台，为开发者提供全链条的 AI 数据支持，应对和解决数据处理中的风险与挑战，推动 AI 研究及应用。

    - [Science Data Bank(ScienceDB)](https://www.scidb.cn/en) : Make your research data citable, discoverable and persistently accessible Satisfy flexible data sharing requirements Dedicate to facilitating data dissemination and reusing. Science Data Bank (ScienceDB) is a public, general-purpose data repository aiming to provide data services (e.g. data acquisition, long-term preservation, publishing, sharing and access) for researchers, research projects/teams, journals, institutions, universities, etc. It supports a variety of data acquisition and data licenses. ScienceDB is dedicated to promoting data findable, citable and reusable on the prerequisite of protecting the rights and interests of data owners and it is built and operated by Computer Network Information Center, Chinese Academy of Sciences.

    - [中国科学数据](http://www.csdata.org/) : 《中国科学数据（中英文网络版）》（China Scientific Data）（CN11-6035/N，ISSN 2096-2223）是目前中国唯一的专门面向多学科领域科学数据出版的学术期刊，作为国家网络连续型出版物的首批试点之一，由中国科学院主管，中国科学院计算机网络信息中心和ISC CODATA中国全国委员会合办，国家科技基础条件平台中心、中国科学院网络安全和信息化领导小组办公室指导，国内外公开发行，中英文，季刊。 中国科学引文数据库（CSCD）来源期刊，中国科技核心期刊 ，收录于中国科协高质量科技期刊分级目录。

    - [飞桨AI Studio](https://aistudio.baidu.com/aistudio/datasetoverview) : 飞桨AI Studio开放数据集。

    - [极市开发者平台](https://www.cvmart.net/dataSets) : 极市开发者平台开放数据集。

    - [openvinotoolkit/datumaro](https://github.com/openvinotoolkit/datumaro)  : Dataset Management Framework, a Python library and a CLI tool to build, analyze and manage Computer Vision datasets.

  - ### Tools

    - #### Data Annotation

        - [Label Studio](https://github.com/HumanSignal/label-studio)  : Label Studio is a multi-type data labeling and annotation tool with standardized output format. [labelstud.io](https://labelstud.io/)

        - [AnyLabeling](https://github.com/vietanhdev/anylabeling)  : Effortless data labeling with AI support from YOLO and Segment Anything! AnyLabeling = LabelImg + Labelme + Improved UI + Auto-labeling.

        - [LabelImg](https://github.com/heartexlabs/labelImg)  : 🖍️ LabelImg is a graphical image annotation tool and label object bounding boxes in images.

        - [labelme](https://github.com/wkentaro/labelme)  : Image Polygonal Annotation with Python (polygon, rectangle, circle, line, point and image-level flag annotation).

        - [DarkLabel](https://github.com/darkpgmr/DarkLabel)  : Video/Image Labeling and Annotation Tool.

        - [AlexeyAB/Yolo_mark](https://github.com/AlexeyAB/Yolo_mark)  : GUI for marking bounded boxes of objects in images for training neural network Yolo v3 and v2.

        - [Cartucho/OpenLabeling](https://github.com/Cartucho/OpenLabeling)  : Label images and video for Computer Vision applications.

        - [CVAT](https://github.com/cvat-ai/cvat)  : Computer Vision Annotation Tool (CVAT). Annotate better with CVAT, the industry-leading data engine for machine learning. Used and trusted by teams at any scale, for data of any scale.

        - [VoTT](https://github.com/Microsoft/VoTT)  : Visual Object Tagging Tool: An electron app for building end to end Object Detection Models from Images and Videos.

        - [WangRongsheng/KDAT](https://github.com/WangRongsheng/KDAT)  : 一个专为视觉方向目标检测全流程的标注工具集，全称：Kill Object Detection Annotation Tools。

        - [Rectlabel-support](https://github.com/ryouchinsa/Rectlabel-support)  : RectLabel - An image annotation tool to label images for bounding box object detection and segmentation.

        - [cnyvfang/labelGo-Yolov5AutoLabelImg](https://github.com/cnyvfang/labelGo-Yolov5AutoLabelImg)  : 💕YOLOV5 semi-automatic annotation tool (Based on labelImg)💕一个基于labelImg及YOLOV5的图形化半自动标注工具。

        - [CVUsers/Auto_maker](https://github.com/CVUsers/Auto_maker)  : 深度学习数据自动标注器开源 目标检测和图像分类（高精度高效率）。

        - [MyVision](https://github.com/OvidijusParsiunas/myvision)  : Computer vision based ML training data generation tool 🚀

        - [wufan-tb/AutoLabelImg](https://github.com/wufan-tb/AutoLabelImg)  : auto-labelimg based on yolov5, with many other useful tools. AutoLabelImg 多功能自动标注工具。

        - [MrZander/YoloMarkNet](https://github.com/MrZander/YoloMarkNet)  : Darknet YOLOv2/3 annotation tool written in C#/WPF.

        - [mahxn0/Yolov3_ForTextLabel](https://github.com/mahxn0/Yolov3_ForTextLabel)  : 基于yolov3的目标/自然场景文字自动标注工具。

        - [MNConnor/YoloV5-AI-Label](https://github.com/MNConnor/YoloV5-AI-Label)  : YoloV5 AI Assisted Labeling.

        - [LILINOpenGitHub/Labeling-Tool](https://github.com/LILINOpenGitHub/Labeling-Tool)  : Free YOLO AI labeling tool. YOLO AI labeling tool is a Windows app for labeling YOLO dataset.

        - [whs0523003/YOLOv5_6.1_autolabel](https://github.com/whs0523003/YOLOv5_6.1_autolabel)  : YOLOv5_6.1 自动标记目标框。

        - [2vin/PyYAT](https://github.com/2vin/PyYAT)  : Semi-Automatic Yolo Annotation Tool In Python.

        - [AlturosDestinations/Alturos.ImageAnnotation](https://github.com/AlturosDestinations/Alturos.ImageAnnotation)  : A collaborative tool for labeling image data for yolo.

        - [stephanecharette/DarkMark](https://github.com/stephanecharette/DarkMark)  : Marking up images for use with Darknet.

        - [2vin/yolo_annotation_tool](https://github.com/2vin/yolo_annotation_tool)  : Annotation tool for YOLO in opencv.

        - [sanfooh/quick_yolo2_label_tool](https://github.com/sanfooh/quick_yolo2_label_tool)  : yolo快速标注工具 quick yolo2 label tool.

        - [folkien/yaya](https://github.com/folkien/yaya)  : YAYA - Yet annother YOLO annoter for images (in QT5). Support yolo format, image modifications, labeling and detecting with previously trained detector.

        - [pylabel-project/pylabel](https://github.com/pylabel-project/pylabel)  : Python library for computer vision labeling tasks. The core functionality is to translate bounding box annotations between different formats-for example, from coco to yolo.

        - [opendatalab/labelU](https://github.com/opendatalab/labelU)  : Uniform, Unlimited, Universal and Unbelievable Annotation Toolbox.

    - #### Data Augmentation

      - [Albumentations](https://github.com/albumentations-team/albumentations)  : Albumentations is a Python library for image augmentation. Image augmentation is used in deep learning and computer vision tasks to increase the quality of trained models. The purpose of image augmentation is to create new training samples from the existing data. "Albumentations: Fast and Flexible Image Augmentations". (**[Information 2020](https://www.mdpi.com/2078-2489/11/2/125)**)

      - [doubleZ0108/Data-Augmentation](https://github.com/doubleZ0108/Data-Augmentation)  : General Data Augmentation Algorithms for Object Detection(esp. Yolo).

    - #### Data Management

      - [YOLOExplorer](https://github.com/lancedb/yoloexplorer)  : YOLOExplorer : Iterate on your YOLO / CV datasets using SQL, Vector semantic search, and more within seconds. Explore, manipulate and iterate on Computer Vision datasets with precision using simple APIs. Supports SQL filters, vector similarity search, native interface with Pandas and more.

## General Detection and Recognition Datasets

  - ### Object Detection Datasets

    - [COCO](https://cocodataset.org/) : "Microsoft COCO: Common Objects in Context". (**[ECCV 2014](https://link.springer.com/chapter/10.1007/978-3-319-10602-1_48)**)

    - [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/) : "The Pascal Visual Object Classes Challenge: A Retrospective". (**[IJCV 2015](https://link.springer.com/article/10.1007/s11263-014-0733-5)**)

    - [Objects365](http://www.objects365.org/overview.html) : "Objects365: A Large-scale, High-quality Dataset for Object Detection". (**[ICCV 2019](https://openaccess.thecvf.com/content_ICCV_2019/html/Shao_Objects365_A_Large-Scale_High-Quality_Dataset_for_Object_Detection_ICCV_2019_paper.html)**)

    - [V3Det](The dataset will be publicly available by June 2023.) : "V3Det: Vast Vocabulary Visual Detection Dataset". (**[arXiv 2023](https://arxiv.org/abs/2304.03752)**)

  - ### Object Recognition Datasets

    - [ImageNet](https://image-net.org/challenges/LSVRC/) : "ImageNet Large Scale Visual Recognition Challenge". (**[IJCV 2015](https://link.springer.com/article/10.1007/s11263-015-0816-y)**)

## Autonomous Driving Datasets

  - ### Diverse Autonomous Driving Datasets

    - [BDD100K](https://bdd-data.berkeley.edu/) : "BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning". (**[CVPR 2020](https://openaccess.thecvf.com/content_CVPR_2020/html/Yu_BDD100K_A_Diverse_Driving_Dataset_for_Heterogeneous_Multitask_Learning_CVPR_2020_paper.html)**)

    - [CODA](https://coda-dataset.github.io/) : "CODA: A Real-World Road Corner Case Dataset for Object Detection in Autonomous Driving". (**[ECCV 2022](https://link.springer.com/chapter/10.1007/978-3-031-19839-7_24)**)

  - ### Traffic Sign Detection Datasets

    - [TT100K](http://cg.cs.tsinghua.edu.cn/traffic-sign/) : "Traffic-Sign Detection and Classification in the Wild". (**[CVPR 2016](https://openaccess.thecvf.com/content_cvpr_2016/html/Zhu_Traffic-Sign_Detection_and_CVPR_2016_paper.html)**)

    - [CCTSDB](https://github.com/csust7zhangjm/CCTSDB)  : CSUST Chinese Traffic Sign Detection Benchmark 中国交通数据集由长沙理工大学综合交通运输大数据智能处理湖南省重点实验室张建明老师团队制作完成。 "A Real-Time Chinese Traffic Sign Detection Algorithm Based on Modified YOLOv2". (**[Algorithms, 2017](https://www.mdpi.com/1999-4893/10/4/127)**)

    - [CCTSDB2021](https://github.com/csust7zhangjm/CCTSDB2021)  : "CCTSDB 2021: a more comprehensive traffic sign detection benchmark". (**[Human-centric Computing and Information Sciences, 2022](https://centaur.reading.ac.uk/106129/)**)

  - ### License Plate Detection and Recognition Datasets

    - [CCPD](https://github.com/detectRecog/CCPD)  : "Towards End-to-End License Plate Detection and Recognition: A Large Dataset and Baseline". (**[ECCV 2018](https://openaccess.thecvf.com/content_ECCV_2018/html/Zhenbo_Xu_Towards_End-to-End_License_ECCV_2018_paper.html)**)

## Adverse Weather Datasets

  - [RESID](https://sites.google.com/site/boyilics/website-builder/reside) : "Benchmarking Single-Image Dehazing and Beyond". (**[IEEE Transactions on Image Processing 2018](https://ieeexplore.ieee.org/abstract/document/8451944)**)

## Person Detection Datasets

  - [INRIA Person](http://lear.inrialpes.fr/data) : "Histograms of oriented gradients for human detection". (**[CVPR 2005](https://ieeexplore.ieee.org/abstract/document/1467360)**)

  - [CrowdHuman](http://www.crowdhuman.org/) : "CrowdHuman: A Benchmark for Detecting Human in a Crowd". (**[arXiv 2018](https://arxiv.org/abs/1805.00123)**)

  - [PANDA](http://www.panda-dataset.com) : "PANDA: A Gigapixel-Level Human-Centric Video Dataset". (**[CVPR 2020](https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_PANDA_A_Gigapixel-Level_Human-Centric_Video_Dataset_CVPR_2020_paper.html)**)

  - [TinyPerson](https://github.com/ucas-vg/PointTinyBenchmark)  : "Scale Match for Tiny Person Detection". (**[WACV 2020](https://openaccess.thecvf.com/content_WACV_2020/html/Yu_Scale_Match_for_Tiny_Person_Detection_WACV_2020_paper.html)**)

  - [TinyPerson v2 | SeaPerson](https://github.com/ucas-vg/PointTinyBenchmark)  : "Object Localization Under Single Coarse Point Supervision". (**[CVPR 2022](https://openaccess.thecvf.com/content/CVPR2022/html/Yu_Object_Localization_Under_Single_Coarse_Point_Supervision_CVPR_2022_paper.html)**)

## Anti-UAV Datasets

  - [Anti-UAV](https://github.com/ZhaoJ9014/Anti-UAV)  : 🔥🔥Official Repository for Anti-UAV🔥🔥. (**[arXiv 2023](https://arxiv.org/abs/2306.15767)**)

  

## Optical Aerial Imagery Datasets

  - [COWC](https://github.com/LLNL/cowc)  : "A large contextual dataset for classification, detection and counting of cars with deep learning". (**[ECCV 2016](https://link.springer.com/chapter/10.1007/978-3-319-46487-9_48)**)

  - [RSOD](https://github.com/RSIA-LIESMARS-WHU/RSOD-Dataset-)  : "Accurate object localization in remote sensing images based on convolutional neural networks". (**[IEEE TGRS 2017](https://ieeexplore.ieee.org/abstract/document/7827088/)**)

  - [LEVIR](http://levir.buaa.edu.cn/Code.htm) : "Random access memories: A new paradigm for target detection in high resolution aerial remote sensing images". (**[IEEE Transactions on Image Processing 2017](https://ieeexplore.ieee.org/abstract/document/8106808)**)

  - [LEVIR-Ship](https://github.com/WindVChen/LEVIR-Ship)  : "A Degraded Reconstruction Enhancement-based Method for Tiny Ship Detection in Remote Sensing Images with A New Large-scale Dataset". (**[IEEE TGRS 2022](https://ieeexplore.ieee.org/abstract/document/9791363)**)

  - [MASATI](https://www.iuii.ua.es/datasets/masati/) : "Automatic ship classification from optical aerial images with convolutional neural networks". (**[Remote Sensing 2018](https://www.mdpi.com/2072-4292/10/4/511)**)

  - [xView](http://xviewdataset.org/) : "xView: Objects in Context in Overhead Imagery". (**[arXiv 2018](https://arxiv.org/abs/1802.07856)**)

  - [DOTA](https://captain-whu.github.io/DOTA/) : "DOTA: A Large-Scale Dataset for Object Detection in Aerial Images". (**[CVPR 2018](https://openaccess.thecvf.com/content_cvpr_2018/html/Xia_DOTA_A_Large-Scale_CVPR_2018_paper.html)**). "Object Detection in Aerial Images: A Large-Scale Benchmark and Challenges". (**[IEEE TPAMI 2021](https://ieeexplore.ieee.org/abstract/document/9560031)**).

  - [ITCVD](https://research.utwente.nl/en/datasets/itcvd-dataset) : "Deep Learning for Vehicle Detection in Aerial Images". (**[IEEE ICIP 2018](https://ieeexplore.ieee.org/abstract/document/8451454)**)

  - [Bridge Dataset](http://www.patreo.dcc.ufmg.br/2019/07/10/bridge-dataset/) : "A Tool for Bridge Detection in Major Infrastructure Works Using Satellite Images". (**[IEEE ICIP 2018](https://ieeexplore.ieee.org/abstract/document/8876942)**)

  - [DIOR](http://www.escience.cn/people/JunweiHan/DIOR.html) : "Object detection in optical remote sensing images: A survey and a new benchmark". (**[ISPRS 2020](https://www.sciencedirect.com/science/article/abs/pii/S0924271619302825)**)

  - [PESMOD](https://github.com/mribrahim/PESMOD)  : "UAV Images Dataset for Moving Object Detection from Moving Cameras". (**[arXiv 2021](https://arxiv.org/abs/2103.11460)**)

  - [AI-TOD](https://github.com/jwwangchn/AI-TOD)  : "Tiny Object Detection in Aerial Images". (**[IEEE ICPR 2021](https://ieeexplore.ieee.org/abstract/document/9413340)**)

  - [RsCarData](https://github.com/ChaoXiao12/Moving-object-detection-DSFNet)  : "DSFNet: Dynamic and Static Fusion Network for Moving Object Detection in Satellite Videos". (**[IEEE GRSL 2021](https://ieeexplore.ieee.org/abstract/document/9594855)**)

  - [VISO](https://github.com/The-Learning-And-Vision-Atelier-LAVA/VISO)  : "Detecting and Tracking Small and Dense Moving Objects in Satellite Videos: A Benchmark". (**[IEEE TGRS 2021](https://ieeexplore.ieee.org/abstract/document/9625976)**)

  - [VisDrone](https://github.com/VisDrone/VisDrone-Dataset)  : "Detection and Tracking Meet Drones Challenge". (**[IEEE TPAMI 2021](https://ieeexplore.ieee.org/abstract/document/9573394)**)

  - [FAIR1M](http://gaofen-challenge.com/benchmark) : "FAIR1M: A benchmark dataset for fine-grained object recognition in high-resolution remote sensing imagery". (**[ISPRS 2021](https://www.sciencedirect.com/science/article/abs/pii/S0924271621003269)**)

  - [SeaDronesSee](https://github.com/Ben93kie/SeaDronesSee)  : "SeaDronesSee: A Maritime Benchmark for Detecting Humans in Open Water". (**[WACV 2022](https://openaccess.thecvf.com/content/WACV2022/html/Varga_SeaDronesSee_A_Maritime_Benchmark_for_Detecting_Humans_in_Open_Water_WACV_2022_paper.html)**)

## Low-light Image Datasets

  - [NightOwls](https://www.nightowls-dataset.org/) : "NightOwls: A Pedestrians at Night Dataset". (**[ACCV 2018](https://link.springer.com/chapter/10.1007/978-3-030-20887-5_43)**).

  - [ExDark](https://github.com/cs-chan/Exclusively-Dark-Image-Dataset)  : "Getting to know low-light images with the exclusively dark dataset". (**[CVIU 2019](https://www.sciencedirect.com/science/article/abs/pii/S1077314218304296)**). "Low-light image enhancement using Gaussian Process for features retrieval". (**[Signal Processing: Image Communication, 2019](https://www.sciencedirect.com/science/article/abs/pii/S0923596518310452)**).

  - [DARK FACE](https://flyywh.github.io/CVPRW2019LowLight/) : DARK FACE: Face Detection in Low Light Condition. "Advancing Image Understanding in Poor Visibility Environments: A Collective Benchmark Study". (**[IEEE Transactions on Image Processing 2020](https://ieeexplore.ieee.org/abstract/document/9049390/)**).

## Infrared Image Datasets

  - [地/空背景下红外图像弱小飞机目标检测跟踪数据集](https://www.scidb.cn/en/detail?dataSetId=720626420933459968) (**[中国科学数据, 2020](http://www.csdata.org/p/387/)**)

  - [复杂背景下红外弱小运动目标检测数据集](https://www.scidb.cn/en/detail?dataSetId=808025946870251520) (**[中国科学数据, 2021](http://www.csdata.org/p/553/)**)

  - [面向空地应用的红外时敏目标检测跟踪数据集](https://www.scidb.cn/en/detail?dataSetId=de971a1898774dc5921b68793817916e) (**[中国科学数据, 2022](http://www.csdata.org/p/673/)**)

  - [SCUT_FIR_Pedestrian_Dataset](https://github.com/SCUT-CV/SCUT_FIR_Pedestrian_Dataset)  : "Benchmarking a large-scale FIR dataset for on-road pedestrian detection". (**[Infrared Physics & Technology, 2019](https://www.sciencedirect.com/science/article/abs/pii/S1350449518305589)**)

  - [NUDT-SIRST](https://github.com/YeRen123455/Infrared-Small-Target-Detection)  : "Dense Nested Attention Network for Infrared Small Target Detection". (**[arXiv 2021](https://arxiv.org/abs/2106.00487)**)

  - [SIRST](https://github.com/YimianDai/sirst)  : "Asymmetric Contextual Modulation for Infrared Small Target Detection". (**[WACV 2021](https://openaccess.thecvf.com/content/WACV2021/html/Dai_Asymmetric_Contextual_Modulation_for_Infrared_Small_Target_Detection_WACV_2021_paper.html)**)

## SAR Image Datasets

  - [SNL VideoSAR](https://www.sandia.gov/radar/pathfinder-radar-isr-and-synthetic-aperture-radar-sar-systems/video/) : "Developments in sar and ifsar systems and technologies at sandia national laboratories". (**[IEEE Aerospace Conference Proceedings, 2003](https://ieeexplore.ieee.org/abstract/document/1235522)**)

  - [MSTAR](https://www.sdms.afrl.af.mil/index.php?collection=mstar) : MSTAR public dataset. "Object recognition results using MSTAR synthetic aperture radar data". (**[IEEE CVBVS 2000](https://ieeexplore.ieee.org/abstract/document/855250/)**)

  - [OpenSARShip](https://opensar.sjtu.edu.cn/) : "OpenSARShip: A Dataset Dedicated to Sentinel-1 Ship Interpretation". (**[IEEE JSTAEORS 2017](https://ieeexplore.ieee.org/abstract/document/8067489)**)

  - [OpenSARShip 2.0](https://opensar.sjtu.edu.cn/) : "OpenSARShip 2.0: A large-volume dataset for deeper interpretation of ship targets in Sentinel-1 imagery". (**[IEEE BIGSARDATA 2017](https://ieeexplore.ieee.org/abstract/document/8124929)**)

  - [SSDD](https://aistudio.baidu.com/aistudio/datasetdetail/54806) : "Ship detection in SAR images based on an improved faster R-CNN". (**[IEEE BIGSARDATA 2017](https://ieeexplore.ieee.org/abstract/document/8124934/)**). "基于深度学习的SAR图像舰船检测数据集及性能分析". (**[第五届高分辨率对地观测学术年会, 2018](https://kns.cnki.net/kcms/detail/detail.aspx?dbcode=CPFD&dbname=CPFDLAST2019&filename=ZKZD201810001014&uniplatform=NZKPT&v=yO0QaBvz14EhL7pk2vCZgRGQl9EUK4g_ZLMv--RusqdnPK4jBUFATMtsDuwGc8fzPb9iLY3lVOI%3d)**)

  - [AIR-SARShip](https://radars.ac.cn/web/data/getData?newsColumnId=1e6ecbcc-266d-432c-9c8a-0b9a922b5e85) : "高分辨率SAR舰船检测数据集-2.0". "AIR-SARShip-1.0: 高分辨率 SAR 舰船检测数据集". (**[雷达学报 2019](https://kns.cnki.net/kcms/detail/detail.aspx?dbcode=CJFD&dbname=CJFDLAST2020&filename=LDAX201906014&uniplatform=NZKPT&v=pL57X-1uWs_T7QAY3gMTKZ1ZrPt1hdyAPDo3jpXRqPLbyAYbrH6-IAZMrqpRwS3J)**)

  - [SAR-Ship-Dataset](https://github.com/CAESAR-Radi/SAR-Ship-Dataset)  : "A SAR Dataset of Ship Detection for Deep Learning under Complex Backgrounds". (**[Remote Sensing, 2019](https://www.mdpi.com/2072-4292/11/7/765)**)

  - [OpenSARUrban](https://opensar.sjtu.edu.cn/) : "OpenSARUrban: A Sentinel-1 SAR Image Dataset for Urban Interpretation". (**[IEEE JSTAEORS 2020](https://ieeexplore.ieee.org/abstract/document/8952866/)**)

  - [HRSID](https://github.com/chaozhong2010/HRSID)  : "HRSID: A High-Resolution SAR Images Dataset for Ship Detection and Instance Segmentation". (**[IEEE Access 2020](https://ieeexplore.ieee.org/abstract/document/9127939)**)

  - [FUSAR-Ship](https://emwlab.fudan.edu.cn/resources/) : 高分辨率船只数据集FUSAR-Ship1.0. (**[雷达学报](https://radars.ac.cn/web/data/getData?dataType=FUSAR)**). "FUSAR-Ship: building a high-resolution SAR-AIS matchup dataset of Gaofen-3 for ship detection and recognition". (**[Science China Information Sciences, 2020](https://link.springer.com/article/10.1007/s11432-019-2772-5)**)

  - [Official-SSDD](https://github.com/TianwenZhang0825/Official-SSDD)  : "SAR Ship Detection Dataset (SSDD): Official Release and Comprehensive Data Analysis ". (**[Remote Sensing, 2021](https://www.mdpi.com/2072-4292/13/18/3690)**)

  - [MSAR](https://radars.ac.cn/web/data/getData?dataType=MSAR) : "大规模多类SAR目标检测数据集-1.0"。(**[雷达学报 2022](https://radars.ac.cn/web/data/getData?dataType=MSAR)**)

  - [RSDD-SAR](https://radars.ac.cn/web/data/getData?dataType=SDD-SAR) : "RSDD-SAR:SAR舰船斜框检测数据集"。(**[雷达学报 2022](https://kns.cnki.net/kcms/detail/detail.aspx?dbcode=CJFD&dbname=CJFDLAST2022&filename=LDAX202204006&uniplatform=NZKPT&v=J3WR8KUVzuYM6uPXqbI64hl8oRAk3mvWRv3hrBCH9ZBek54uYq_UkJGY0PGaaxDg)**)

## Multispectral Image Datasets

  - [FLIR_ADAS](https://adas-dataset-v2.flirconservator.com/) : Teledyne FLIR Free ADAS Thermal Dataset v2.

  - [VEDAI](https://downloads.greyc.fr/vedai/) : "Vehicle Detection in Aerial Imagery: A small target detection benchmark". (**[Journal of Visual Communication and Image Representation 2015](https://hal.archives-ouvertes.fr/hal-01122605v2/document)**)

  - [KAIST_rgbt](https://github.com/SoonminHwang/rgbt-ped-detection)  : "Multispectral Pedestrian Detection: Benchmark Dataset and Baseline". (**[CVPR 2015](https://openaccess.thecvf.com/content_cvpr_2015/html/Hwang_Multispectral_Pedestrian_Detection_2015_CVPR_paper.html)**)

  - [TNO](https://figshare.com/articles/dataset/TNO_Image_Fusion_Dataset/1008029) : "The TNO multiband image data collection". (**[Data in brief, 2017](https://www.data-in-brief.com/article/S2352-3409(17)30469-9/abstract)**)

  - [MFNet](https://github.com/haqishen/MFNet-pytorch)  : MFNet-pytorch, image semantic segmentation using RGB-Thermal images. "MFNet: Towards real-time semantic segmentation for autonomous vehicles with multi-spectral scenes". (**[IROS 2017](https://ieeexplore.ieee.org/abstract/document/8206396/)**). ([MFNet Dataset](https://www.mi.t.u-tokyo.ac.jp/static/projects/mil_multispectral/) : Multi-spectral Object Detection and Semantic Segmentation Datasets)

  - [LLVIP](https://github.com/bupt-ai-cz/LLVIP)  : "LLVIP: A Visible-Infrared Paired Dataset for Low-Light Vision". (**[ICCV 2021](https://openaccess.thecvf.com/content/ICCV2021W/RLQ/html/Jia_LLVIP_A_Visible-Infrared_Paired_Dataset_for_Low-Light_Vision_ICCVW_2021_paper.html)**)

  - [MSRS](https://github.com/Linfeng-Tang/MSRS)  : MSRS: Multi-Spectral Road Scenarios for Practical Infrared and Visible Image Fusion. "[PIAFusion](https://github.com/Linfeng-Tang/PIAFusion) : A progressive infrared and visible image fusion network based on illumination aware". (**[Information Fusion, 2022](https://www.sciencedirect.com/science/article/abs/pii/S156625352200032X)**)

  - [TarDAL](https://github.com/JinyuanLiu-CV/TarDAL)  : "Target-Aware Dual Adversarial Learning and a Multi-Scenario Multi-Modality Benchmark To Fuse Infrared and Visible for Object Detection". (**[CVPR 2022](https://openaccess.thecvf.com/content/CVPR2022/html/Liu_Target-Aware_Dual_Adversarial_Learning_and_a_Multi-Scenario_Multi-Modality_Benchmark_To_CVPR_2022_paper.html)**). ([M3FD Dataset](https://drive.google.com/drive/folders/1H-oO7bgRuVFYDcMGvxstT1nmy0WF_Y_6?usp=sharing))

  - [DroneVehicle](https://github.com/VisDrone/DroneVehicle)  : "Drone-based RGB-Infrared Cross-Modality Vehicle Detection via Uncertainty-Aware Learning". (**[IEEE TCSVT 2022](https://ieeexplore.ieee.org/abstract/document/9759286/)**)

## 3D Object Detection Datasets

  - [Objectron](https://github.com/google-research-datasets/Objectron)  : "Objectron: A Large Scale Dataset of Object-Centric Videos in the Wild with Pose Annotations". (**[CVPR, 2021](https://openaccess.thecvf.com/content/CVPR2021/html/Ahmadyan_Objectron_A_Large_Scale_Dataset_of_Object-Centric_Videos_in_the_CVPR_2021_paper.html?ref=https://githubhelp.com)**)

## Vehicle-to-Everything Field Datasets

  - [OpenCOOD|OPV2V](https://github.com/DerrickXuNu/OpenCOOD)  : OpenCOOD is an Open COOperative Detection framework for autonomous driving. It is also the official implementation of the ICRA 2022 paper [OPV2V](https://mobility-lab.seas.ucla.edu/opv2v/). "OPV2V: An Open Benchmark Dataset and Fusion Pipeline for Perception with Vehicle-to-Vehicle Communication". (**[ICRA, 2022](https://ieeexplore.ieee.org/abstract/document/9812038/)**). [mobility-lab.seas.ucla.edu/opv2v/](https://mobility-lab.seas.ucla.edu/opv2v/)

  - [CoBEVT](https://github.com/DerrickXuNu/CoBEVT)  : "CoBEVT: Cooperative Bird's Eye View Semantic Segmentation with Sparse Transformers". (**[CoRL, 2022](https://arxiv.org/abs/2207.02202)**).

  - [Where2comm](https://github.com/MediaBrain-SJTU/where2comm)  : "Where2comm: Communication-Efficient Collaborative Perception via Spatial Confidence Maps". (**[Neurips, 2022](https://arxiv.org/abs/2209.12836)**).

  - [PJLab-ADG/LiDARSimLib-and-Placement-Evaluation](https://github.com/PJLab-ADG/LiDARSimLib-and-Placement-Evaluation)  : "Analyzing Infrastructure LiDAR Placement with Realistic LiDAR Simulation Library". (**[ICRA, 2023](https://arxiv.org/abs/2211.15975)**).

  - [CoAlign](https://github.com/yifanlu0227/CoAlign)  : "Robust Collaborative 3D Object Detection in Presence of Pose Errors". (**[ICRA, 2023](https://arxiv.org/abs/2211.07214)**).

  - [V2V4Real](https://github.com/ucla-mobility/V2V4Real)  : "V2V4Real: A Real-World Large-Scale Dataset for Vehicle-to-Vehicle Cooperative Perception". (**[CVPR, 2023](https://openaccess.thecvf.com/content/CVPR2023/html/Xu_V2V4Real_A_Real-World_Large-Scale_Dataset_for_Vehicle-to-Vehicle_Cooperative_Perception_CVPR_2023_paper.html)**).

  - [V2X-ViT|V2XSet](https://github.com/DerrickXuNu/v2x-vit)  : "V2X-ViT: Vehicle-to-Everything Cooperative Perception with Vision Transformer". (**[ECCV, 2022](https://link.springer.com/chapter/10.1007/978-3-031-19842-7_7)**).

  - [DAIR-V2X](https://github.com/AIR-THU/DAIR-V2X)  : "DAIR-V2X: A Large-Scale Dataset for Vehicle-Infrastructure Cooperative 3D Object Detection". (**[CVPR, 2022](https://openaccess.thecvf.com/content/CVPR2022/html/Yu_DAIR-V2X_A_Large-Scale_Dataset_for_Vehicle-Infrastructure_Cooperative_3D_Object_Detection_CVPR_2022_paper.html)**). [全球首个车路协同自动驾驶数据集发布](https://thudair.baai.ac.cn)

  - [V2X-Seq](https://github.com/AIR-THU/DAIR-V2X-Seq)  : "V2X-Seq: A Large-Scale Sequential Dataset for Vehicle-Infrastructure Cooperative Perception and Forecasting". (**[CVPR, 2023](https://openaccess.thecvf.com/content/CVPR2023/html/Yu_V2X-Seq_A_Large-Scale_Sequential_Dataset_for_Vehicle-Infrastructure_Cooperative_Perception_and_CVPR_2023_paper.html)**). [全球首个大规模时序车路协同自动驾驶数据集发布](https://thudair.baai.ac.cn)

## Super-Resolution Field Datasets

  - [VideoLQ](https://github.com/ckkelvinchan/RealBasicVSR)  : "Investigating Tradeoffs in Real-World Video Super-Resolution". (**[CVPR, 2022](https://openaccess.thecvf.com/content/CVPR2022/html/Chan_Investigating_Tradeoffs_in_Real-World_Video_Super-Resolution_CVPR_2022_paper.html)**)

## Face Detection and Recognition Datasets

  - ### Face Detection Datasets

    - [WIDER FACE](http://shuoyang1213.me/WIDERFACE/) : "WIDER FACE: A Face Detection Benchmark". (**[CVPR 2016](https://openaccess.thecvf.com/content_cvpr_2016/html/Yang_WIDER_FACE_A_CVPR_2016_paper.html)**)

    - [UFDD](https://ufdd.info/) : Unconstrained Face Detection Dataset(UFDD). "Pushing the Limits of Unconstrained Face Detection: a Challenge Dataset and Baseline Results". (**[IEEE BTAS 2018](https://ieeexplore.ieee.org/abstract/document/8698561l)**)

  - ### Face Recognition Datasets

    - [LFW](http://vis-www.cs.umass.edu/lfw/) : Labeled Faces in the Wild(LFW). "Labeled Faces in the Wild: A Database forStudying Face Recognition in Unconstrained Environments". (**[Workshop on faces in'Real-Life'Images: detection, alignment, and recognition. 2008](https://hal.inria.fr/inria-00321923/)**)

    - [YouTube Faces (YTF)](http://www.cs.tau.ac.il/~wolf/ytfaces/) : "Face recognition in unconstrained videos with matched background similarity". (**[CVPR 2011](https://ieeexplore.ieee.org/abstract/document/5995566)**)

    - [CASIA-WebFace](https://pan.baidu.com/s/1k3Cel2wSHQxHO9NkNi3rkg) : "Learning Face Representation from Scratch". (**[arXiv 2014](https://arxiv.org/abs/1411.7923)**)

    - [IJB-A](https://www.nist.gov/programs-projects/face-challenges) : "Pushing the Frontiers of Unconstrained Face Detection and Recognition: IARPA Janus Benchmark A". (**[CVPR 2015](https://www.cv-foundation.org/openaccess/content_cvpr_2015/html/Klare_Pushing_the_Frontiers_2015_CVPR_paper.html)**)

    - [MS-Celeb-1M](https://academictorrents.com/details/9e67eb7cc23c9417f39778a8e06cca5e26196a97/tech&hit=1&filelist=1) : "MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition". (**[ECCV 2016](https://link.springer.com/chapter/10.1007/978-3-319-46487-9_6)**)

    - [MegaFace](http://megaface.cs.washington.edu/) : "The MegaFace Benchmark: 1 Million Faces for Recognition at Scale". (**[CVPR 2016](https://openaccess.thecvf.com/content_cvpr_2016/html/Kemelmacher-Shlizerman_The_MegaFace_Benchmark_CVPR_2016_paper.html)**)

    - [UMDFaces](https://www.umdfaces.io/) : "UMDFaces: An annotated face dataset for training deep networks". (**[IJCB 2017](https://ieeexplore.ieee.org/abstract/document/8272731)**)

    - [IJB-B](https://www.nist.gov/programs-projects/face-challenges) : "IARPA Janus Benchmark-B Face Dataset". (**[CVPR 2017](https://openaccess.thecvf.com/content_cvpr_2017_workshops/w6/html/Whitelam_IARPA_Janus_Benchmark-B_CVPR_2017_paper.html)**)

    - [IJB-C](https://www.nist.gov/programs-projects/face-challenges) : "IARPA Janus Benchmark - C: Face Dataset and Protocol". (**[ICB 2018](https://ieeexplore.ieee.org/abstract/document/8411217)**)

    - [VGGFace2]() : "VGGFace2: A Dataset for Recognising Faces across Pose and Age". (**[FG 2018](https://ieeexplore.ieee.org/abstract/document/8373813)**)

## Blogs

  - 微信公众号「PandaCVer」

    - [2022-11-01, 目标检测算法——行人检测&人群计数数据集汇总(附下载链接)](https://mp.weixin.qq.com/s/8eDJ86rPA-0cWnLQKHxfjw)

    - [2022-11-21, 目标检测算法——工业缺陷数据集汇总1（附下载链接）](https://mp.weixin.qq.com/s/oRmPDF1YhIqYYdrgU7sTUQ)

    - [2022-12-01, 目标检测算法——图像分类开源数据集汇总（附下载链接）](https://mp.weixin.qq.com/s/9tGzWDAxp--42ofmKLlJRg)

  - 微信公众号「自动驾驶之心」

    - [2023-03-27, 目标跟踪方向开源数据集资源汇总](https://mp.weixin.qq.com/s/dCwtc-DI0KaPB4meJqewwA)

    - [2023-04-12, 包罗万象！V3Det：1.3W类全新目标检测数据集（港中文&上海AI Lab）](https://mp.weixin.qq.com/s/A-4ze7B3yQ-AYCe0DgHv-A)

  - 微信公众号「整数智能AI研究院」

    - [2022-03-10, 最全自动驾驶数据集分享系列一｜目标检测数据集（1/3）](https://mp.weixin.qq.com/s/eoMa1eUXPaZBlHeZReR77A)

    - [2022-03-21, 最全自动驾驶数据集分享系列一｜目标检测数据集（2/3）](https://mp.weixin.qq.com/s/nJFG6GHw60pRODoEKWj3bg)

    - [2022-04-24, 最全自动驾驶数据集分享系列一｜目标检测数据集（3/3）](https://mp.weixin.qq.com/s/r9d7NmcA3dymKRUhWoIPzw)