Omkar2118/datasets huggingface dataset:
Lombardia Sentinel-2 Image Time Series for Crop Mapping

Usually, the information related to the crop types available in a given territory is annual information, that is, we only know the type of main crop grown over a year and we do not know any crops that have followed one another during the year and also we do not know when a particular crop is sown and when it is harvested.
The main objective of this dataset is to create the basis for experimenting with suitable solutions to give a reliable answer to the above questions, or to propose models capable of producing dynamic segmentation maps that show when a crop begins to grow and when it is collected. Consequently, being able to understand if more than one crop has been grown in a territory within a year.
In this dataset, we have 20 coverage classes as ground-truth values provided by Regine Lombardia.
The mapping of the class labels used (see file lombardia-classes/classes25pc.txt) brings together some classes and provides the time intervals within which that category grows.
The last two columns of the following table are respectively the date (month-day) of the start and end of the interval in which the class is visible during the construction of our dataset.

Introducing paper: In-season and dynamic crop mapping using 3D convolution neural networks and sentinel-2 time series
Text2shape

A large dataset of natural language descriptions for physical 3D objects in the ShapeNet dataset.
ShapeGlot
Full name: ShapeGlot: Learning Language for Shape Differentiation

ShapeGlot: Learning Language for Shape Differentiation

Introducing paper: ShapeGlot: Learning Language for Shape Differentiation
THU-FVFDT
Full name: Tsinghua University Finger Vein and Finger Dorsal Texture Database

**THU-FVFDT** is a dataset containing raw finger vein and finger dorsal texture images of 220 different subjects. Images are captured in two different sessions with interval of about dozens of seconds. One session is for training and the other for testing. Four finger vein images and four finger dorsal texture images are captured simultaneously in each session. We only offer one of four images in that there is approximately no difference between them. The size of raw images is 720×576 pixels.