Omkar2118/datasets huggingface dataset:
Kinetics-700-2020

We describe the 2020 edition of the DeepMind Kinetics
human action dataset, which replenishes and extends the
Kinetics-700 dataset. In this new version, there are at least
700 video clips from different YouTube videos for each of
the 700 classes. This paper details the changes introduced
for this new release of the dataset and includes a comprehensive set of statistics as well as baseline results using the
I3D network.

Introducing paper: A Short Note on the Kinetics-700-2020 Human Action Dataset
NoLiMa
Full name: NoLiMa: Long-Context Evaluation Beyond Literal Matching

A benchmark extending needle-in-a-haystack (NIAH) test with a carefully designed needle set, where questions and needles have minimal lexical overlap, requiring models to infer latent associations to locate the needle within the haystack.

Introducing paper: NoLiMa: Long-Context Evaluation Beyond Literal Matching
GTA-Human II

This is the latest version of our datasets, and is built upon GTA-V for expressive human pose and shape estimation. It features multi-person scenes with SMPL-X annotations. In addition to color image sequences, 3D bounding boxes and cropped point clouds (generated from synthetic depth images) are also provided. Please contact Zhongang Cai (caiz0023@e.ntu.edu.sg) for feedback.

Introducing paper: Playing for 3D Human Recovery
EnvBench

EnvBench is a comprehensive benchmark for **automating environment setup** - an important task in software engineering. We have collected the largest dataset to date for this task and introduced a robust framework for developing and evaluating LLM-based agents that tackle environment setup challenges.

Our benchmark includes:

* **994 repositories**: 329 Python and 665 JVM-based (Java, Kotlin) projects
* **Genuine configuration challenges**: Carefully selected repositories that cannot be configured with simple deterministic scripts
* **Evaluation metrics**: Static analysis for missing imports in Python and compilation checks for JVM languages
* **Baselines**: Zero-shot baselines and agentic workflows tested with GPT-4o and GPT-4o-mini

Introducing paper: EnvBench: A Benchmark for Automated Environment Setup