Omkar2118/datasets huggingface dataset:
UI5k
Full name: Mobile App User Interface Dataset

This dataset contains 54,987 UI screenshots and the metadata from 7,748 Android applications belonging to 25 application categories

Download link: [https://www.dropbox.com/sh/kfkhevxykzwputb/AAAhL6ipmOg4zZn4jUL_myF0a?dl=0](https://www.dropbox.com/sh/kfkhevxykzwputb/AAAhL6ipmOg4zZn4jUL_myF0a?dl=0)

Introducing paper: Wireframe-Based UI Design Search Through Image Autoencoder
NEWSKVQA

**NEWSKVQA** is a new dataset of 12K news videos spanning across 156 hours with 1M multiple-choice question-answer pairs covering 8263 unique entities.

Introducing paper: NEWSKVQA: Knowledge-Aware News Video Question Answering
Drone vs Bird
Full name: Drone vs Bird Detection Challenge

For the Drone-vs-Bird Detection Challenge 2021, 77 different video sequences have been made available as training data. These video sequences originate from the previous installment of the challenge and were collected using MPEG4-coded static cameras by the SafeShore project, by the Fraunhofer IOSB research institute and by the ALADDIN2 project. On average, the video sequences consist of 1,384 frames, while each frame contains 1.12 annotated drones. The video sequences are recorded with both static cameras and moving cameras and the resolution varies between 720×576 and 3840×2160 pixels. In total, 8 different types of drones exist in the dataset , i.e. 3 with fixed wings and 5 rotary ones. For each video, a separate annotation file is provided, which contains the frame number and the bounding box (expressed as [topx topy width height]) for the frames in which drones enter the scenes.

Source: [Drone-vs-Bird Detection Challenge at IEEE AVSS2021
](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9663844&casa_token=yyGQLesXf_QAAAAA:sKtn96xAwk_63tadtkOjNIivpIwXZ-fXJ1if8zzXcy4ZbEwfwq-8udMsldorBUfmr0xO9e9Ubg&tag=1)
DELAUNAY

**DELAUNAY** is a dataset of abstract paintings and non-figurative art objects labelled by the artists' names. This dataset provides a middle ground between natural images and artificial patterns and can thus be used in a variety of contexts, for example to investigate the sample efficiency of humans and artificial neural networks.

The dataset comprises 11,503 images from 53 categories, i.e. artists (mean number of images per artist: 217.04; standard deviation: 58.55), along with the associated URLs. These samples are split between a training set of 9202 images and a test set of 2301 images.

Introducing paper: DELAUNAY: a dataset of abstract art for psychophysical and machine learning research
Medical Question Pairs
Full name: Medical Question Pairs (MQP) Dataset

# Medical Question Pairs (MQP) Dataset

This repository contains a dataset of 3048 similar and dissimilar medical question pairs hand-generated and labeled by Curai's doctors. The dataset is described in detail in [our paper](https://drive.google.com/file/d/1CHPGBXkvZuZc8hpr46HeHU6U6jnVze-s/view).

## Methodology
We present our doctors with a list of 1524 patient-asked questions randomly sampled from the publicly available crawl of [HealthTap](https://github.com/durakkerem/Medical-Question-Answer-Datasets). Each question results in one similar and one different pair through the following instructions provided to the labelers:

1. Rewrite the original question in a different way while maintaining the same intent. Restructure the syntax as much as possible and change medical details that would not impact your response.
 e.g. "I'm a 22-y-o female" could become "My 26 year old daughter"
2. Come up with a related but dissimilar question for which the answer to the original question would be WRONG OR IRRELEVANT. Use similar key words.

The first instruction generates a _positive_ question pair (similar) and the second generates a _negative_ question pair (different). With the above instructions, we intentionally frame the task such that positive question pairs can look very different by superficial metrics, and negative question pairs can conversely look very similar. This ensures that the task is not trivial.

## Dataset format
The dataset is formatted as `dr_id, question_1, question_2, label`. We used 11 different doctors for this task so `dr_id` ranges from 1 to 11. The label is 1 if the question pair is similar and 0 otherwise.

## Dataset statistics
The final dataset contains 4567 unique questions. The minimum, maximum, median and average number of tokens in these questions are 4, 81, 20 and 22.675 respectively showing there is reasonable variance in the length of the questions. The shortest question is `Are fibroadenomas malignant?`

An off-the-shelf medical entity recognizer finds around 1000 unique medical entities in the questions. Some of the top entity mentions were: `physician, pregnancy, pain, lasting weeks, menstruation, emotional state, cancer, visual function, headache, bleeding, fever, sexual intercourse`

Introducing paper: Effective Transfer Learning for Identifying Similar Questions: Matching User Questions to COVID-19 FAQs