Omkar2118/datasets huggingface dataset:
Multilingual Fever

https://github.com/lingo-iitgn/XME

Introducing paper: Cross-lingual Editing in Multilingual Language Models
LOL-Blur
Full name: low-blur/high-sharp-scaled

A total of 170 videos for training and 30 videos for testing, each of which has 60 frames, amounting to 12,000 paired data. (Note that the first and last 30 frames of each video are NOT consecutive, and their darknesses are simulated differently as well.)

Introducing paper: LEDNet: Joint Low-light Enhancement and Deblurring in the Dark
SICE-Mix

A test dataset SICE_Mix image datasets to represent complex mixed over-/under-exposed scenes.

Introducing paper: Low-Light Image and Video Enhancement: A Comprehensive Survey and Beyond
SICE-Grad

A test dataset SICE_Grad image datasets to represent complex mixed over-/under-exposed scenes.

Introducing paper: Low-Light Image and Video Enhancement: A Comprehensive Survey and Beyond
Sony-Total-Dark
Full name: SID Sony subset without gamma correction

Original SID dataset is introduced in "Learning to See in the Dark". 
The subset of SID dataset captured by Sony α7S II camera is adopted for evaluation. There are 2697 short-long-exposure RAW image pairs. To make this dataset more challenging, we converted the RAW format images to sRGB images with no gamma correction, which resulted in images becoming extremely dark.

Introducing paper: You Only Need One Color Space: An Efficient Network for Low-light Image Enhancement
MMVP-VLM

The **MMVP-VLM (Multimodal Visual Patterns - Visual Language Models) Benchmark** is specifically designed to systematically evaluate the performance of recent **CLIP-based models** in understanding and processing visual patterns. Let's delve into the details:

- **Purpose**: The MMVP-VLM Benchmark aims to assess how well CLIP models can match **image-text combinations** that represent distinct visual patterns. It distills a subset of questions from the original MMVP benchmark into simpler language descriptions, categorizing them into different visual patterns. Each visual pattern is represented by **15 text-image pairs**.

- **Dataset Composition**:
    - **Text-Image Pairs**: The benchmark includes a balanced number of questions for each visual pattern, with each pattern represented by 15 pairs. These pairs are a subset of the MMVP benchmark, supplemented with additional questions for balance.
    - **Visual Patterns**: The questions cover various visual patterns, allowing evaluation of CLIP models' ability to understand and process these patterns.

- **Insights and Limitations**: By assessing whether CLIP models can accurately match the provided image-text combinations, the MMVP-VLM Benchmark provides insights into the capabilities and limitations of these models.

Introducing paper: Eyes Wide Shut? Exploring the Visual Shortcomings of Multimodal LLMs
DangerousQA

DangerousQA refers to a set of harmful questions used to evaluate the safety and behavior of large language models (LLMs) in generating responses. In the context of the RED-EVAL safety benchmark, DangerousQA consists of 200 harmful questions collected from various sources, such as those related to racism, stereotypes, sexism, legality, toxicity, and harm. These questions are used to test the ability of LLMs to handle sensitive and potentially harmful content and to assess their performance in generating appropriate responses to such prompts.

Introducing paper: Red-Teaming Large Language Models using Chain of Utterances for Safety-Alignment
Arxiv-5Lbench

Arxiv dataset

Introducing paper: MORAL: Aligning AI with Human Norms through Multi-Objective Reinforced Active Learning