Omkar2118/datasets huggingface dataset:
CSL-News

A large-scale gloss-free sign language translation dataset with 1,985 hours of videos, approximately 86 times larger than the previous CSL-Daily dataset.

Introducing paper: Uni-Sign: Toward Unified Sign Language Understanding at Scale
Sample Dataset and Trained Model Parameters for Back-Projection Diffusion

We have uploaded a sample dataset for training and testing Back-Projection Diffusion. Trained model parameters for the dataset are also provided in tmp.zip.

Introducing paper: Back-Projection Diffusion: Solving the Wideband Inverse Scattering Problem with Diffusion Models
Waldo and Wenda

A benchmark for Human-Human Interaction (HHI) recognition as free text.

Introducing paper: Learning Human-Human Interactions in Images from Weak Textual Supervision
MP-IDB
Full name: MP-IDB: The Malaria Parasite Image Database for Image Processing and Analysis

MP-IDB comprises four species of Malaria parasites: Falciparum, Malariae, Ovale, Vivax. For each species, there are four distinct stages of life, described in the filenames as follows:

R: indicates the presence of 1 or more Ring stage parasites
T: indicates the presence of 1 or more Trophozoite stage parasites
S: indicates the presence of 1 or more Schizont stage parasites
G: indicates the presence of 1 or more Gametocyte stage parasites.
Each original image has its own ground-truth, provided by expert pathologists, while every image has been analyzed from left to right, therefore every filename indicates the presence of the parasites following this indication.
M5-Malaria Dataset

In this project, we tried to make malaria detection easily possible at a low cost. We present M5-malaria Dataset which is the first-ever dataset that is across microscopes and across magnifications. Malaria, a fatal but curable disease claims hundreds of thousands of lives every year. Early and correct diagnosis is vital to avoid health complexities, however, it depends upon the availability of costly microscopes and trained experts to analyze blood-smear slides. Deep learning-based methods have the potential to not only decrease the burden of experts but also improve diagnostic accuracy on low-cost microscopes. However, this is hampered by the absence of a reasonable size dataset. One of the most challenging aspects is the reluctance of the experts to annotate the dataset at low magnification on low-cost microscopes. We present a dataset to further the research on malaria microscopy over the low-cost microscopes at low magnification. Our large-scale dataset consists of images of blood-smear slides from several malaria-infected patients, collected through microscopes at two different cost spectrums and multiple magnifications. Malarial cells are annotated for the localization and life-stage classification task.