Omkar2118/datasets huggingface dataset:
CIFAR-10N
Full name: Real-World Human Annotations

This work presents two new benchmark datasets (CIFAR-10N, CIFAR-100N), equipping the training dataset of CIFAR-10 and CIFAR-100 with human-annotated real-world noisy labels that we collect from Amazon Mechanical Turk.

Introducing paper: Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotations
CIFAR-100N
Full name: Real-World Human Annotations

This work presents two new benchmark datasets (CIFAR-10N, CIFAR-100N), equipping the training dataset of CIFAR-10 and CIFAR-100 with human-annotated real-world noisy labels that we collect from Amazon Mechanical Turk.

Introducing paper: Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotations
IndoNLG

IndoNLG is a benchmark to measure natural language generation (NLG) progress in three low-resource—yet widely spoken—languages of Indonesia: Indonesian, Javanese, and Sundanese. Altogether, these languages are spoken by more than 100 million native speakers, and hence constitute an important use case of NLG systems today. Concretely, IndoNLG covers six tasks: summarization, question answering, chit-chat, and three different pairs of machine translation (MT) tasks.

Introducing paper: IndoNLG: Benchmark and Resources for Evaluating Indonesian Natural Language Generation
Continual World

Continual World is a benchmark consisting of realistic and meaningfully diverse robotic tasks built on top of Meta-World as a testbed.

Introducing paper: Continual World: A Robotic Benchmark For Continual Reinforcement Learning
Natural Instructions

Natural-Instructions is a dataset of 61 distinct tasks, their human-authored instructions and 193k task instances. The instructions are obtained from crowdsourcing instructions used to create existing NLP datasets and mapped to a unified schema.

Introducing paper: Cross-Task Generalization via Natural Language Crowdsourcing Instructions
Pyxis

Pyxis is a performance dataset for specialized accelerators on sparse data. Pyxis collects accelerator designs and real execution performance statistics. Currently, there are 73.8 K instances in Pyxis.

Introducing paper: Pyxis: An Open-Source Performance Dataset of Sparse Accelerators
OPERAnet

**OPERAnet** is a multimodal activity recognition dataset acquired from radio frequency and vision-based sensors. Approximately 8 hours of annotated measurements are provided, which are collected across two different rooms from 6 participants performing 6 activities, namely, sitting down on a chair, standing from sit, lying down on the ground, standing from the floor, walking and body rotating. The dataset has been acquired from four synchronized modalities for the purpose of passive Human Activity Recognition (HAR) as well as localization and crowd counting.

Introducing paper: OPERAnet: A Multimodal Activity Recognition Dataset Acquired from Radio Frequency and Vision-based Sensors
AI-TOD
Full name: Tiny Object Detection in Aerial Images

AI-TOD comes with 700,621 object instances for eight categories across 28,036 aerial images. Compared to existing object detection datasets in aerial images, the mean size of objects in AI-TOD is about 12.8 pixels, which is much smaller than others.

Introducing paper: Tiny Object Detection in Aerial Images
URLB
Full name: Unsupervised Reinforcement Learning Benchmark

URLB consists of two phases: reward-free pre-training and downstream task adaptation with extrinsic rewards. Building on the DeepMind Control Suite, it provides twelve continuous control tasks from three domains for evaluation.

Introducing paper: URLB: Unsupervised Reinforcement Learning Benchmark
CoVA
Full name: CoVA dataset for Webpage Object Detection / Information Extraction

We labeled _7,740_ webpage screenshots spanning _408_ domains (Amazon, Walmart, Target, etc.). Each of these webpages contains exactly one labeled price, title, and image. All other web elements are labeled as background. On average, there are _90_ web elements in a webpage.

Webpage screenshots and bounding boxes can be obtained [here](https://drive.google.com/drive/folders/1LQPXGhDVh40bIT2-LZfo498M93tidABe?usp=sharing)

### Train-Val-Test split
We create a cross-domain split which ensures that each of the train, val and test sets contains webpages from different domains. Specifically, we construct a 3 : 1 : 1 split based on the number of distinct domains. We observed that the top-5 domains (based on number of samples) were Amazon, EBay, Walmart, Etsy, and Target. So, we created 5 different splits for 5-Fold Cross Validation such that each of the major domains is present in one of the 5 splits for test data.

Introducing paper: CoVA: Context-aware Visual Attention for Webpage Information Extraction