Omkar2118/datasets huggingface dataset:
Cam-CAN
Full name: Cambridge Centre for Ageing and Neuroscience dataset

The Cambridge Centre for Ageing and Neuroscience (Cam-CAN) is a large-scale collaborative research project at the University of Cambridge, launched in October 2010, with substantial initial funding from the Biotechnology and Biological Sciences Research Council (BBSRC), followed by support from the Medical Research Council (MRC) Cognition & Brain Sciences Unit (CBU) and the European Union Horizon 2020 LifeBrain project. The Cam-CAN project uses epidemiological, cognitive, and neuroimaging data to understand how individuals can best retain cognitive abilities into old age.

https://camcan-archive.mrc-cbu.cam.ac.uk/dataaccess/
TRR360D
Full name: TRR360D

**TRR360D** is based on the ICDAR2019MTD modern table detection dataset, it refers to the annotation format of the DOTA dataset. The training set contains 600 rotated images and 977 annotated instances, and the test set contains 240 rotated images and 499 annotated instances.

Source: [T360RRD: A dataset for 360 degree rotated rectangular box table detection](https://arxiv.org/pdf/2303.01894v2.pdf)

Image Source: [https://github.com/vansin/TRR360D](https://github.com/vansin/TRR360D)

Introducing paper: TRR360D: A dataset for 360 degree rotated rectangular box table detection
UESTC-MMEA-CL
Full name: A multi-modal egocentric activity dataset for continual learning

UESTC-MMEA-CL is a new multi-modal activity dataset for continual egocentric activity recognition, which is proposed to promote future studies on continual learning for first-person activity recognition in wearable applications. Our dataset provides not only vision data with auxiliary inertial sensor data but also comprehensive and complex daily activity categories for the purpose of continual learning research. UESTC-MMEA-CL comprises 30.4 hours of fully synchronized first-person video clips, acceleration stream and gyroscope data in total. There are 32 activity classes in the dataset and each class contains approximately 200 samples. We divide the samples of each class into the training set, validation set and test set according to the ratio of 7:2:1. For the continual learning evaluation, we present three settings of incremental steps, i.e., the 32 classes are divided into {16, 8, 4} incremental steps and each step contains {2, 4, 8} activity classes, respectively.

Introducing paper: Towards Continual Egocentric Activity Recognition: A Multi-modal Egocentric Activity Dataset for Continual Learning