Omkar2118/datasets huggingface dataset:
AiTLAS: Benchmark Arena

**AiTLAS: Benchmark Arena** is an open-source benchmark framework for evaluating state-of-the-art deep learning approaches for image classification in Earth Observation (EO).
Nocturne

Nocturne is  a 2D, partially observed, driving simulator, built in C++ for speed and exported as a Python library.

It is currently designed to handle traffic scenarios from the Waymo Open Dataset, and with some work could be extended to support different driving datasets. Using the Python library nocturne, one is able to train controllers for AVs to solve various tasks from the Waymo dataset, which we provide as a benchmark, then use the tools we offer to evaluate the designed controllers.

Using this rich data source, Nocturne contains a wide range of scenarios whose solution requires the formation of complex coordination, theory of mind, and handling of partial observability. Below we show replays of the expert data, centered on the light blue agent, with the corresponding view of the agent on the right.

Description from: [Nocturne](https://github.com/facebookresearch/nocturne)

Introducing paper: Nocturne: a scalable driving benchmark for bringing multi-agent learning one step closer to the real world