Omkar2118/datasets huggingface dataset:
RTMV

**RTMV** is a large-scale synthetic dataset for novel view synthesis consisting of ∼300k images rendered from nearly 2000 complex scenes using high-quality ray tracing at high resolution (1600 × 1600 pixels). The dataset is orders of magnitude larger than existing synthetic datasets for novel view synthesis, thus providing a large unified benchmark for both training and evaluation. Using 4 distinct sources of high-quality 3D meshes, the scenes of our dataset exhibit challenging variations in camera views, lighting, shape, materials, and textures.

The dataset consists of scenes from four different environments, namely Google Scanned Objects, ABC, Bricks and Amazon Berkeley. Each scene has 150 renders at a 1600 x 1600 resolution.

Description adapted from: [http://www.cs.umd.edu/~mmeshry/projects/rtmv/](http://www.cs.umd.edu/~mmeshry/projects/rtmv/)

Introducing paper: RTMV: A Ray-Traced Multi-View Synthetic Dataset for Novel View Synthesis
Image Description Sequences

A dataset of description sequences, a sequence of expressions that together are meant to single out one image from an (imagined) set of other similar images. These sequences were produced in a monological setting, but with the instruction to imagine they were provided to a partner who successively asked for more information (hence, tell me more).

Introducing paper: Tell Me More: A Dataset of Visual Scene Description Sequences
Twitter MediaEval
Full name: MediaEval Benchmarking Initiative for Multimedia Evaluation

The task addresses the problem of the appearance and propagation of posts that share misleading multimedia content (images or video).
In the context of the task, different types of misleading use are considered:

1. Reposting of real multimedia, such as real photos from the past re-posted as being associated to a current event,
2. Digitally manipulated multimedia,
3. Synthetic multimedia, such as artworks or snapshots presented as real imagery.

Source: http://www.multimediaeval.org/mediaeval2016/verifyingmultimediause/
GWA
Full name: Geometric-Wave Acoustic

GWA is a large-scale audio dataset of over 2 million synthetic room impulse responses (IRs) and their corresponding detailed geometric and simulation configurations. Our dataset samples acoustic environments from over 6.8K high-quality diverse and professionally designed houses represented as semantically labeled 3D meshes
PGDP5K
Full name: Plane Geometry Diagram Parsing Dataset

PGDP5K is a dataset consisting of 5000 diagram samples composed of 16 shapes, covering 5 positional relations, 22 symbol types and 6 text types, labeled with more fine-grained annotations at primitive level, including primitive classes, locations and relationships, where 1,813 non-duplicated images are selected from the Geometry3K dataset and other 3,187 images are collected from three popular textbooks across grades 6-12 on mathematics curriculum websites by taking screenshots from PDF books.

Introducing paper: Plane Geometry Diagram Parsing
BurstSR

BurstSR is a dataset consisting of smartphone bursts and high-resolution DSLR ground-truth

Introducing paper: Deep Burst Super-Resolution
WikiBanEvasion
Full name: Wikipedia Ban Evasion Dataset

A dataset comprising 8,551 ban evasion pairs on Wikipedia, where each pair comprises a parent account and the child account. We adopt a strategy to ensure that there is a 1:1 mapping between parent and child accounts. For each of the accounts in these ban evasion pairs, we provide the following data:
- Wikipedia usernames, creation date, ban date, and other account-level meta-data
- Corresponding edit information in form of revision IDs, pages edited, added text, deleted text, edit comment, and timestamp

Introducing paper: Characterizing, Detecting, and Predicting Online Ban Evasion
Penn94

Node classification on Penn94

Introducing paper: Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods
genius

node classification on genius

Introducing paper: Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods
twitch-gamers

node classification on twitch-gamers

Introducing paper: Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods
NICO++

The goal of NICO Challenge is to facilitate the OOD (Out-of-Distribution) generalization in visual recognition through promoting the research on the intrinsic learning mechanisms with native invariance and generalization ability. The training data is a mixture of several observed contexts while the test data is composed of unseen contexts. Participants are tasked with developing reliable algorithms across different contexts (domains) to improve the generalization ability of models.

Introducing paper: NICO++: Towards Better Benchmarking for Domain Generalization
MFQE v2
Full name: Multi-Frame Quality Enhancement v2 Dataset

A dataset for compressed video quality enhancement.

Introducing paper: MFQE 2.0: A New Approach for Multi-frame Quality Enhancement on Compressed Video
Water Footprint Recommender System Data

It contains data from two different realities: Food.com, a well-known American recipe site, and Planeat, an Italian site that allows you to plan recipes to save food waste. The dataset is divided into two parts: embeddings, which can be used directly to execute the work and receive suggestions, and raw data, which must first be processed into embeddings.

Introducing paper: Food Recommendations for Reducing Water Footprint