We introduce EvalAI, an open source platform for evaluating and comparing machine learning (ML) and artificial intelligence algorithms (AI) at scale. EvalAI is built to provide a scalable solution to the research community to fulfill the critical need of evaluating machine learning models and agents acting in an environment against annotations or with a human-in-the-loop. This will help researchers, students, and data scientists to create, collaborate, and participate in AI challenges organized around the globe. By simplifying and standardizing the process of benchmarking these models, EvalAI seeks to lower the barrier to entry for participating in the global scientific effort to push the frontiers of machine learning and artificial intelligence, thereby increasing the rate of measurable progress in this domain.
@article{arxiv.1902.03570,
title = {EvalAI: Towards Better Evaluation Systems for AI Agents},
author = {Deshraj Yadav and Rishabh Jain and Harsh Agrawal and Prithvijit Chattopadhyay and Taranjeet Singh and Akash Jain and Shiv Baran Singh and Stefan Lee and Dhruv Batra},
journal= {arXiv preprint arXiv:1902.03570},
year = {2019}
}