EvalAI is an open-source platform for evaluating machine learning and AI algorithms and hosting research challenges, built by the Cloud-CV team. Organizers can create competitions with custom evaluation metrics, phases and leaderboards, while participants submit predictions or code and get scored automatically and reproducibly. EvalAI powers the hosted eval.ai service used by many academic challenges, and the codebase has around 2,000 GitHub stars with ongoing development into 2026.
Running a fair ML competition is mostly infrastructure: dataset hosting, hidden test sets, submission queues, evaluation workers and anti-cheat controls. EvalAI packages all of that into a Django and Angular application with Docker-based evaluation workers, so a research group can launch a challenge in days instead of months. It supports both prediction-file submissions and code-upload submissions, remote evaluation on the participant’s own cluster, and detailed per-phase leaderboards with public and private splits.
Free. EvalAI is open source, and the hosted eval.ai platform is free for challenge organizers and participants; self-hosting only costs your own infrastructure.
Research labs, conference workshops and companies that run ML benchmarks or competitions, and researchers who want a standardized, reproducible way to compare models.
EvalAI grew out of the Cloud-CV community at Virginia Tech and Georgia Tech and has hosted challenges attached to major venues, including workshop competitions at top computer vision and machine learning conferences. Organizers get an admin dashboard for reviewing teams and submissions, support for multiple concurrent phases with different datasets, and the option to keep evaluation code private while publishing only aggregate scores, which has made it a default choice for academic benchmarking.