Omkar2118/datasets huggingface dataset:
POIE
Full name: Products for OCR and Information Extraction

Products for OCR and Information Extraction (POIE) dataset derives from camera images of various products in the real world. The images are carefully selected and manually annotated. Our labeling team consists of 8 experienced labelers. We first crop the nutrition tables from product images and adopt multiple commercial OCR engines (Azure and Baidu OCR) for pre-labeling. Then we use LabelMe  to manually check the annotation of the location as well as transcription of every text box, and the values of entities for all the text in the images and repaired the OCR errors found. After discarding low-quality and blurred images, we obtain 3,000 images with 111,155 text instances.

from https://github.com/jfkuang/cfam
ESP
Full name: Evaluation for Styled Prompt

ESP dataset (Evaluation for Styled Prompt dataset) is a benchmark for zero-shot domain-conditional caption generation. ESP is a new dataset focusing on providing multiple styled text targets for the same image. It comprises 4.8k captions from 1k images in the COCO Captions test set. We collect five text domains with everyday usage: blog, social media, instruction, story, and news.

Introducing paper: Multimodal Knowledge Alignment with Reinforcement Learning
AndroDrift

Dataset for the paper entitled "Efficient Concept Drift Handling for Batch Android Malware Detection Models". Contains 100 monthly goodware and malware samples between january of 2012 and december of 2019. The training set used consist of samples for the full 2012 year, whereas the remaining data is used for evaluation purposes on a quaterly basis.
RT-Percept Sun Temple

Pre-rendered dataset used in Training and Predicting Visual Error for Real-Time Applications for the Sun Temple scene. Generated using the RT-Percept renderer and the RT-Percept scenes.

Introducing paper: Training and Predicting Visual Error for Real-Time Applications
RT-Percept Lumberyard Bistro

Pre-rendered dataset used in Training and Predicting Visual Error for Real-Time Applications for the Lumberyard Bistro scenes. Generated using the RT-Percept renderer and the RT-Percept scenes.

Introducing paper: Training and Predicting Visual Error for Real-Time Applications
RT-Percept Emerald Square

Pre-rendered dataset used in Training and Predicting Visual Error for Real-Time Applications for the Emerald Square scenes. Generated using the RT-Percept renderer and the RT-Percept scenes.

Introducing paper: Training and Predicting Visual Error for Real-Time Applications
RT-Percept Sibenik Cathedral

Pre-rendered dataset used in Training and Predicting Visual Error for Real-Time Applications for the Sibenik Cathedral scene. Generated using the RT-Percept renderer and the RT-Percept scenes.

Introducing paper: Training and Predicting Visual Error for Real-Time Applications