Resources

	MALS Dataset [website] We present a large Multi-Attribute and Language Search dataset for text-based person retrieval, called MALS, and explore the feasibility of performing pre-training on both attribute recognition and image-text matching tasks in one stone. In particular, MALS contains 1, 510, 330 image-text pairs, which is about 37.5× larger than prevailing CUHK-PEDES, and all images are annotated with 27 attributes.
	University-1652 Dataset [website] [SoTA] We collect 1652 buildings of 72 universities around the world. University-1652 contains data from three platforms, i.e., synthetic drones, satellites and ground cameras of 1,652 university buildings around the world. To our knowledge, University-1652 is the first drone-based geo-localization dataset and enables two new tasks, i.e., drone-view target localization and drone navigation.
	Market-1501 and DukeMTMC-reID Attribute Datasets [website] We manually annotate attribute labels for two large-scale re-ID datasets, and systematically investigate how person re-ID and attribute recognition benefit from each other.
	3D Market-1501 Dataset [website] You could find the point-cloud format Market-1501 Dataset at https://github.com/layumi/person-reid-3d.
	DG-Market Dataset We provide our generated images and make a large-scale synthetic dataset called DG-Market. This dataset is generated by our [DG-Net](https://arxiv.org/abs/1904.07223) and consists of 128,307 images (613MB), about 10 times larger than the training set of original Market-1501 (even much more can be generated with DG-Net). It can be used as a source of unlabeled training dataset for semi-supervised learning. You may download the dataset from [Google Drive] (or [Baidu Disk]) password: qxyh).

HQ-Market Super-resolution Dataset. [website]
DukeMTMC-reID Dataset. [website] [SoTA]
DukeMTMC-Pose Dataset. [website]
UTS Person-reID Tutorial. [website]

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