深度学习数据集
在这里我们汇集一些可供下载的深度学习数据集
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医学图像数据集汇总 || 医学图像分类分割数据集合集 || 278 dataset for segmentation
118 dataset for Image Classification || 25 dataset for Medical Image Segmentation
Paperwithcode网站把4,083 machine learning datasets一网打尽
Kaggle Datasets ||
MedMNIST v2 (说明) || MedMNIST v2 （数据下载） || MedMNIST （GitHub Code）
MedMNIST Classification Decathlon: A Lightweight AutoML Benchmark for Medical Image Analysis
最著名的： ImageNet
【数据集介绍】ImageNet介绍_谢军的博客-CSDN博客_imagenet
Pytorch 图像分类实战 —— ImageNet 数据集 | 大专栏
图像分割/图像分割数据集：
PASCAL VOC |
MS COCO |
Cityscapes |
ADE20k |
目标检测数据集PASCAL VOC简介
MS COCO数据集详解_持久决心的博客-CSDN博客
Dataset之Cityscapes：Cityscapes数据集的简介、安装 ...
Dataset之ADE20k：ADE20k数据集的简介、安装、使用方法 ..
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医学图像分割数据集
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| ADE20K （20,210/2,000） | LUNA （267） | PROMISE12 | ||
| NIH-CT-82 (7,141) | GlaS （85/80） | ACDC | ||
| CHAOS MRI （992）& CT （2,874） | DRISHTI-GS (50/51) | |||
| REFUGE (400/400) | DRIVE (20/20) | |||
| STARE (397) | HRF (45) | |||
ADE20K： Bolei Zhou, Hang Zhao, Xavier Puig, Sanja Fidler, Adela Barriuso, and Antonio Torralba. Semantic understanding of scenes through the ade20k dataset. arXiv preprint arXiv:1608.05442, 2016.
NIH-CT-82： Holger R Roth, Le Lu, Amal Farag, and Hoo-Chang Shin, et al. Deeporgan: Multi-level deep convolutional networks for automated pancreas segmentation. In International con ference on medical image computing and computer-assisted intervention, pages 556–564. Springer, 2015
CHAOS （MRI & CT）： Ali Emre Kavur, M. Alper Selver, O˘guz Dicle, Mustafa Barıs¸, and N. Sinem Gezer. CHAOS - Combined (CT-MR)
Healthy Abdominal Organ Segmentation Challenge Data, Apr. 2019.
REFUGE： Jos´e Ignacio Orlando, Huazhu Fu, Jo˜ao Barbossa Breda, and Karel van Keer, et al. Refuge challenge: A unified framework for evaluating automated methods for glaucoma assessment from fundus photographs. Medical image analysis, 59:101570, 2020.
STARE： A. D. Hoover, Valentina Kouznetsova, and Michael Goldbaum. Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response. IEEE Transactions on Medical Imaging, 19(3):203–210, 2000.
LUNA： Arnaud Arindra Adiyoso Setio, Alberto Traverso, Thomas De Bel, Moira SN Berens, Cas van den Bogaard, Piergiorgio Cerello, Hao Chen, Qi Dou, Maria Evelina Fantacci, Bram Geurts, et al. Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in
computed tomography images: the luna16 challenge. Medi
cal image analysis, 42:1–13, 2017.
GlaS： Korsuk Sirinukunwattana, David RJ Snead, and Nasir MRajpoot. A stochastic polygons model for glandular structures in colon histology images. IEEE transactions on medical imaging, 34(11):2366–2378, 2015.
DRISHTI-GS： Jayanthi Sivaswamy, S. R. Krishnadas, Gopal Datt Joshi, Madhulika Jain, and A. Ujjwaft Syed Tabish. Drishti-gs: Retinal image dataset for optic nerve head(onh) segmentation. In IEEE International Symposium on Biomedical Imag ing, 2014
DRIVE： Joes Staal,Michael D Abr`amoff,Meindert Niemeijer,Max A Viergever, and Bram Van Ginneken. Ridge-based vessel segmentation in color images of the retina. IEEE transactions on medical imaging, 23(4):501–509, 2004.
HRF： Attila Budai, R¨udiger Bock, Andreas Maier, Joachim Hornegger, and Georg Michelson. Robust vessel segmentation in fundus images. International journal of biomedical imaging, 2013, 2013.
PROMISE12： Geert Litjens, Robert Toth, Wendy van de Ven, Caroline Hoeks, Sjoerd Kerkstra, Bram van Ginneken, Graham Vincent, Gwenael Guillard, Neil Birbeck, Jindang Zhang, et al. Evaluation of prostate segmentation algorithms for mri: the promise12 challenge. Medical image analysis, 18(2):359–
373, 2014.
ACDC： Olivier Bernard, Alain Lalande, Clement Zotti, Frederick Cervenansky, Xin Yang, Pheng-Ann Heng, Irem Cetin, Karim Lekadir, Oscar Camara, Miguel Angel Gonzalez Ballester, et al. Deep learning techniques for automatic mri cardiac multi-structures segmentation and diagnosis: is the problem solved? IEEE transactions on medical imaging, 37(11):2514–2525, 2018.
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医学图像分类数据集
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CheXpert : Jeremy Irvin, Pranav Rajpurkar, Michael Ko, and Yifan Yu, et al. Chexpert: A large chest radiograph dataset with uncertainty labels and expert comparison.
ChestXRay 2017: Daniel S Kermany, Michael Goldbaum, Wenjia Cai, Carolina CS Valentim, Huiying Liang, Sally L Baxter, Alex McKeown, Ge Yang, Xiaokang Wu, Fangbing Yan, et al. Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell, 172(5):1122–1131, 2018.
EyePACS: Kaggle EyePACS Competition. Identify signs of diabetic retinopathy in eye images. Available at: https://www.kaggle.com/c/diabetic-retinopathy-detection/ [Accessed 4 May. 2020], 2015.
BUSI: Walid Al-Dhabyani, Mohammed Gomaa, Hussien Khaled, and Aly Fahmy. Dataset of breast ultrasound images. Data in brief, 28:104863, 2020.
Retinal OCT: Daniel S Kermany, Michael Goldbaum, Wenjia Cai, Carolina CS Valentim, Huiying Liang, Sally L Baxter, Alex McKeown, Ge Yang, Xiaokang Wu, Fangbing Yan, et al. Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell, 172(5):1122–1131, 2018.
MESSIDOR: Etienne Decenci`ere, Xiwei Zhang, Guy Cazuguel, Bruno Lay, B´eatrice Cochener, Caroline Trone, Philippe Gain, Richard Ordonez, Pascale Massin, Ali Erginay, et al. Feedback on a publicly distributed image database: the messidor database. Image Analysis & Stereology, 33(3):231–234,
2014.
CIFAR100: Alex Krizhevsky, Geoffrey Hinton, et al. Learning multiple layers of features from tiny images. 2009.
ImageNet: Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. Imagenet classification with deep convolutional neural networks. In Advances in neural information processing sys tems, pages 1097–1105, 2012.
ADE20K： Bolei Zhou, Hang Zhao, Xavier Puig, Sanja Fidler, Adela Barriuso, and Antonio Torralba. Semantic understanding of scenes through the ade20k dataset. arXiv preprint arXiv:1608.05442, 2016.
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物体检测数据集
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755 dataset for Object detection
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实例分割数据集
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414 dataset for Instance Segmentation
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全景分割数据集
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Dataset之ADE20k：ADE20k数据集的简介、安装、使用方法之详细攻略
Dataset之COCO数据集：COCO数据集的简介、下载、使用方法之详细攻略
Dataset之MapillaryVistas：MapillaryVistas数据集的简介、下载、使用方法之详细攻略
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DNA序列数据集
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| Barcode of Life Data System (BOLD) | RDP Ribosomal Database Project |