Omkar2118/datasets huggingface dataset:
MoCap
Full name: CMU Graphics Lab Motion Capture Database

Collection of various motion capture recordings (walking, dancing, sports, and others) performed by over 140 subjects. The database contains free motions which you can download and use. There is a zip file of all asf/amc's on the FAQs page.

Source: [https://www.re3data.org/repository/r3d100012183](https://www.re3data.org/repository/r3d100012183)
USF
Full name: Human ID Gait Challenge Dataset

The **USF** **Human ID Gait Challenge Dataset** is a dataset of videos for gait recognition. It has videos from 122 subjects in up to 32 possible combinations of variations in factors.

Source: [http://www.eng.usf.edu/cvprg/Gait_Data.html](http://www.eng.usf.edu/cvprg/Gait_Data.html)

Introducing paper: The HumanID Gait Challenge Problem: Data Sets, Performance, and Analysis
MALF
Full name: Multi-Attribute Labelled Faces

The **MALF** dataset is a large dataset with 5,250 images annotated with multiple facial attributes and it is specifically constructed for fine grained evaluation.

Source: [Pushing the Limits of Unconstrained Face Detection:a Challenge Dataset and Baseline Results](https://arxiv.org/abs/1804.10275)
Image Source: [http://www.cbsr.ia.ac.cn/faceevaluation/](http://www.cbsr.ia.ac.cn/faceevaluation/)

Introducing paper: Fine-grained evaluation on face detection in the wild
Oxford-Affine

The **Oxford-Affine** dataset is a small dataset containing 8 scenes with sequence of 6 images per scene. The images in a sequence are related by homographies.

Source: [A Large Dataset for Improving Patch Matching](https://arxiv.org/abs/1801.01466)
Image Source: [https://www.robots.ox.ac.uk/~vgg/data/affine/](https://www.robots.ox.ac.uk/~vgg/data/affine/)

Introducing paper: A Comparison of Affine Region Detectors
DispScenes

The **DispScenes** dataset was created to address the specific problem of disparate image matching. The image pairs in all the datasets exhibit high levels of variation in illumination and viewpoint and also contain instances of occlusion. The DispScenes dataset provides manual ground truth keypoint correspondences for all images.

Source: [Matching Disparate Image Pairs Using Shape-Aware ConvNets](https://arxiv.org/abs/1811.09889)

Introducing paper: Deep Spectral Correspondence for Matching Disparate Image Pairs
Retrieval-SfM

The Retrieval-SFM dataset is used for instance image retrieval. The dataset contains 28559 images from 713 locations in the world. Each image has a label indicating the location it belongs to. Most locations are famous man-made architectures such as palaces and towers, which are relatively static and positively contribute to visual place recognition. The training dataset contains various perceptual changes including variations in viewing angles, occlusions and illumination conditions, etc.

Source: [Localizing Discriminative Visual Landmarks for Place Recognition](https://arxiv.org/abs/1904.06635)

Introducing paper: CNN Image Retrieval Learns from BoW: Unsupervised Fine-Tuning with Hard Examples
VGG Cell

The **VGG Cell** dataset (made up entirely of synthetic images) is the main public benchmark used to compare cell counting techniques.

Source: [People, Penguins and Petri Dishes: Adapting Object Counting Models To New Visual Domains And Object Types Without Forgetting](https://arxiv.org/abs/1711.05586)
Image Source: [https://www.robots.ox.ac.uk/~vgg/research/counting/index_org.html](https://www.robots.ox.ac.uk/~vgg/research/counting/index_org.html)

Introducing paper: Microscopy cell counting and detection with fully convolutional regression networks