Omkar2118/datasets huggingface dataset:
Representative PDE Benchmarks

Given the lack of consensus on a standard setof benchmarks for machine learning of PDEs, we propose a new suite of benchmarks here. Our aims in this regard are to ensure 
i) sufficient diversity among the types of PDE considered
ii) access to training and test data is readily available for rapid prototyping and reproducibility
iii) intrinsic computational complexity of problem to make sure that it is worthwhile to
design fast surrogates to classical PDE solvers for a particular problem

Introducing paper: Convolutional Neural Operators for robust and accurate learning of PDEs
VisIT-Bench

VisIT-Bench is a new vision-language instruction following benchmark inspired by real-world use cases. Testing 70 diverse “wish-list” skills with an automated ranking system, it advances the ongoing assessment of multimodal chatbot performance.


Why VisIT-Bench 🤔?
Though recent VLMs have shown promise in following instructions, their evaluation for real-world human-chatbot instructions is often limited. Typically, VLMs are evaluated through qualitative comparison of outputs, which makes it challenging to quantify progress and potential shortcomings. VisIT-Bench helps address this problem by offering a comprehensive testbed for measuring model performance across a diverse set of instruction-following tasks, inspired by real world scenarios.🌍

Introducing paper: VisIT-Bench: A Benchmark for Vision-Language Instruction Following Inspired by Real-World Use
LLaVA-Bench

LLaVA-Bench is a dataset created to evaluate the capability of large multimodal models (LMM) in more challenging tasks and generalizability to novel domains. It consists of a diverse set of 24 images with 60 questions in total, including indoor and outdoor scenes, memes, paintings, sketches, etc., and each image with a highly-detailed and manually-curated description and a proper selection of questions. The dataset is part of the LLaVA project, which aims to develop multimodal chatbots that follow human intents to complete various daily-life visual tasks in the wild.

Introducing paper: Visual Instruction Tuning
Algonauts 2023

The Algonauts 2023 Challenge focuses on predicting responses in the human brain as participants perceive complex natural visual scenes. Through collaboration with the Natural Scenes Dataset (NSD) team, the Challenge runs on the largest suitable brain dataset available, opening new venues for data-hungry modeling.
Soccer
Full name: ISSIA-CNR Soccer

- This dataset was originally introduced by [1] for soccer ball and player tracking from six synchronized videos. 
- Since ball annotations provided by [1] are collapsed, new annotations of ball 2D coordinates are provided by [2]
- For sports ball detection and tracking evaluation, the first four video clips are used for training and the remaining two clips are for testing.

[1] T. D’Orazio et al., A Semi-automatic System for Ground Truth Generation of Soccer Video Sequences, in AVSS, 2009.
[2] S. Tarashima et al., Widely Applicable Strong Baseline for Sports Ball Detection and Tracking, in BMVC, 2023.

Introducing paper: A Semi-automatic System for Ground Truth Generation of Soccer Video Sequences
Tennis

This dataset was introduced by [1], but was not used in its experiment. 
[2] propose to use all the clips included in the first 7 games as a training set, and the remainings as a testing set.

[1] Y.-C. Huang et al., TrackNet: A Deep Learning Network for Tracking High-speed and Tiny Objects in Sports Applications. AVSS, 2019.

[2] S. Tarashima et al., Widely Applicable Strong Baseline for Sports Ball Detection and Tracking, BMVC, 2023.

Introducing paper: TrackNet: A Deep Learning Network for Tracking High-speed and Tiny Objects in Sports Applications