Omkar2118/datasets huggingface dataset:
iLIDS-VID
Full name: iLIDS-VID

The **iLIDS-VID** dataset is a person re-identification dataset which involves 300 different pedestrians observed across two disjoint camera views in public open space. It comprises 600 image sequences of 300 distinct individuals, with one pair of image sequences from two camera views for each person. Each image sequence has variable length ranging from 23 to 192 image frames, with an average number of 73. The iLIDS-VID dataset is very challenging due to clothing similarities among people, lighting and viewpoint variations across camera views, cluttered background and random occlusions.

Source: [http://www.eecs.qmul.ac.uk/~xiatian/downloads_qmul_iLIDS-VID_ReID_dataset.html](http://www.eecs.qmul.ac.uk/~xiatian/downloads_qmul_iLIDS-VID_ReID_dataset.html)
Image Source: [http://www.eecs.qmul.ac.uk/~xiatian/downloads_qmul_iLIDS-VID_ReID_dataset.html](http://www.eecs.qmul.ac.uk/~xiatian/downloads_qmul_iLIDS-VID_ReID_dataset.html)

Introducing paper: Unsupervised Person Re-identification by Deep Learning Tracklet Association
CUHK01
Full name: CUHK Person Re-identification

This dataset contains 971 identities from two disjoint camera views. Each identity has two samples per camera view. It is used for Person Re-identification.

Paper: [Li W., Zhao R., Wang X. (2013) Human Reidentification with Transferred Metric Learning. In: Lee K.M., Matsushita Y., Rehg J.M., Hu Z. (eds) Computer Vision – ACCV 2012. ACCV 2012. Lecture Notes in Computer Science, vol 7724. Springer, Berlin, Heidelberg](https://doi.org/10.1007/978-3-642-37331-2_3)
PRID2011
Full name: Person RE-ID 2011

PRID 2011 is a person reidentification dataset that provides multiple person trajectories recorded from two different static surveillance cameras, monitoring crosswalks and sidewalks. The dataset shows a clean background, and the people in the dataset are rarely occluded. In the dataset, 200 people appear in both views. Among the 200 people, 178 people have more than 20 appearances

Source: [PaMM: Pose-aware Multi-shot Matching for Improving Person Re-identification](https://arxiv.org/abs/1705.06011)

Introducing paper: Mask R-CNN
SUN397
Full name: SUN397

The Scene UNderstanding (SUN) database contains 899 categories and 130,519 images. There are 397 well-sampled categories to evaluate numerous state-of-the-art algorithms for scene recognition.

Image Source: [The Selection of Useful Visual Words in Class-Imbalanced Image Classification](https://www.semanticscholar.org/paper/The-Selection-of-Useful-Visual-Words-in-Image-Chimlek-Pramokchon/2b23cff4d2072dfc85cf8b09f54475791690a68d)