Omkar2118/datasets huggingface dataset:
BanMANI

A Dataset to Identify Manipulated Social Media News in Bangla

We construct a publicly available Bangla dataset of 800 news-related social media items that are annotated as manipulated or not relative to 500 reference news articles. We present a semi-automatic (use both human and LLM) method for generating such a dataset, which allows scalable dataset collection using annotators efficiently for languages with few available NLP tools.

Introducing paper: BanMANI: A Dataset to Identify Manipulated Social Media News in Bangla
Polyp ASH

This dataset was built with data acquired at the Hospital Clinic of Barcelona, Spain. It is composed of a total of 1126 HD polyp images. There are a total of 473 unique polyps, with a variable number of different shots per polyp (minimum: 2, maximum: 24, median: 10). Special attention was paid to ensure that images from the same polyp show different conditions. An external frame-grabber and a white light endoscope were used to capture raw images. The dataset contains images with two different resolutions: 1920 x 1080 and 1350 x 1080.

Introducing paper: Towards Fine-Grained Polyp Segmentation and Classification
FoodSG-233
Full name: Localized Singaporean Food Image Dataset

The **FoodSG-233** dataset contains 209,861 images, covering 13 food groups and 233 food categories. 

Specifically, food groups are more coarse-grained whereas food categories are more fine-grained, e.g., the group “Sugars, sweets and confectionery” contains categories such as “Parfait” and “Popcorn”. We guarantee that there are at least 400 food images per food category.

With a specific focus on localized Singaporean cuisine, the FoodSG-233 dataset aims to promote future research directions for the data management community in food computing.

Introducing paper: From Plate to Prevention: A Dietary Nutrient-aided Platform for Health Promotion in Singapore