Omkar2118/datasets huggingface dataset:
ViMQ

**ViMQ** is a Vietnamese dataset of medical questions from patients with sentence-level and entity-level annotations for the Intent Classification and Named Entity Recognition tasks. It contains Vietnamese medical questions crawled from the consultation section online between patients and doctors from www.vinmec.com, a website of a Vietnamese general hospital. Each consultation consists of a question regarding a specific health issue of a patient and a detailed respond provided by a clinical expert. The dataset contains health issues that fall into a wide range of categories including common illness, cardiology, hematology, cancer, pediatrics, etc. We removed sections where users ask about information of the hospital and selected 9,000 questions for the dataset.

Source: [ViMQ: A Vietnamese Medical Question Dataset for Healthcare Dialogue System Development](https://arxiv.org/pdf/2304.14405v1.pdf)

Introducing paper: ViMQ: A Vietnamese Medical Question Dataset for Healthcare Dialogue System Development
LIS
Full name: low-light instance segmentation

To reveal and systematically investigate the effectiveness of the proposed method in the real world, a real low-light image dataset for instance segmentation is necessary and urgently needed.
Considering there is no suitable dataset, therefore, we collect and annotate a Low-light Instance Segmentation (LIS) dataset using a Canon EOS 5D Mark IV camera.

It exhibits the following characteristics:

Paired samples.  In the LIS dataset, we provide images in both sRGB-JPEG (typical camera output) and RAW formats, each format consists of paired short-exposure low-light and corresponding long-exposure normal-light images. We term these four types of images as \textit{sRGB-dark, sRGB-normal, RAW-dark, and RAW-normal}. To ensure they are pixel-wise aligned, we mount the camera on a sturdy tripod and avoid vibrations by remote control via a mobile app.  

Diverse scenes.  The LIS dataset consists of 2230 image pairs, which are collected in various scenes, including indoor and outdoor. To increase the diversity of low-light conditions, we use a series of ISO levels (\eg, 800, 1600, 3200, 6400) to take long-exposure reference images, and we deliberately decrease the exposure time by a series of low-light factors (\eg, 10, 20, 30, 40, 50, 100) to take short-exposure images for simulating very low-light conditions. 

Instance-level pixel-wise labels.  For each pair of images, we provide precise instance-level pixel-wise labels annotated by professional annotators, yielding 10504 labeled instances of 8 most common object classes in our daily life (bicycle, car, motorcycle, bus, bottle, chair, dining table, tv).

Introducing paper: Instance Segmentation in the Dark