Omkar2118/datasets huggingface dataset:
SCARED
Full name: Stereo Correspondence and Reconstruction of Endoscopic Data

Sub-Challenge
Part of the Endoscopic Vision Challenge
E-IC

This dataset, adapted from COCO Caption, is designed for the *Image Caption* task  and evaluates multimodal model editing in terms of reliability, stability and generality. You can download the dataset from [here](https://drive.google.com/drive/folders/1jBdTJxUb9wEeHnvG-RY8dv5_I4QlDpUS?usp=drive_link)
E-VQA

This dataset, adapted from VQAv2, is designed for the *Visual Question Answering* task  and evaluates multimodal model editing in terms of reliability, stability and generality. You can download the dataset from [here](https://drive.google.com/drive/folders/1jBdTJxUb9wEeHnvG-RY8dv5_I4QlDpUS?usp=drive_link)
CHAMMI
Full name: CHAMMI: A benchmark for channel-adaptive models in microscopy imaging

We present a cellular microscopic image dataset for investigating channel-adaptive models. We collected and pre-processed images from three publicly available sources: 1) the WTC-11 hiPSC dataset from the Allen Institute (Viana et al., 2023), 2) the Human Protein Atlas dataset (Thul et al., 2017), and 3) a combined Cell Painting dataset from the Broad Institute (Gustafsdottir et al., 2013; Bray et al., 2017; Way et al., 2021). These images contain 3, 4, or 5 channels with different cellular structures highlighted in each channel. The goal of this dataset is to facilitate the creation and evaluation of novel computer vision models that are invariant to channel numbers.
ChiMed-VL

# ChiMed-VL Dataset

## ChiMed-VL-Alignment dataset ##

ChiMed-VL-Alignment consists of 580,014 image-text couplings, each pair falling into one of two categories: context information of an image or descriptions of an image. The context category contains 167M tokens, presenting a median text length of 435 (Q1: 211, Q3: 757). Conversely, descriptions, more concise and image-specific, contain inline descriptions and captions. They comprise 63M tokens, with median lengths settling at 59 (Q1: 45, Q3: 83).

## ChiMed-VL-Instruction dataset ##

ChiMed-VL-Instruction comprises 469,441 question-answer pairs. Within this subset, the questions section contains 10M tokens with a median length of 20 (Q1: 16, Q3: 25), posing a concise inquiry reflective of medical queries. The answers consist of 13M tokens with a median length slightly longer at 22 (Q1: 12, Q3: 34), providing clear, direct, and informative responses.

Introducing paper: Qilin-Med-VL: Towards Chinese Large Vision-Language Model for General Healthcare
X-CLAIM

A multilingual dataset for the task of multilingual claim span identification. 

X-CLAIM consists of 7K real-world claims, and social media posts containing them, collected from various social media platforms (e.g., Instagram) in English, Hindi, Punjabi, Tamil, Telugu and Bengali.

Introducing paper: Lost in Translation, Found in Spans: Identifying Claims in Multilingual Social Media
Condensed Movies

A large-scale video dataset, featuring clips from movies with detailed captions.

Introducing paper: Condensed Movies: Story Based Retrieval with Contextual Embeddings