Request model evaluation on COCO val 2017 dataset
Compare audio representation models using benchmark results
Track, rank and evaluate open LLMs and chatbots
Explore and submit models using the LLM Leaderboard
Push a ML model to Hugging Face Hub
Retrain models for new data at edge devices
Calculate GPU requirements for running LLMs
Optimize and train foundation models using IBM's FMS
Search for model performance across languages and benchmarks
Evaluate RAG systems with visual analytics
Convert Stable Diffusion checkpoint to Diffusers and open a PR
Evaluate model predictions with TruLens
Benchmark models using PyTorch and OpenVINO
The Open Object Detection Leaderboard is a benchmarking platform designed to evaluate and compare different object detection models. It provides a standardized framework for assessing model performance using the COCO (Common Objects in Context) val 2017 dataset. This leaderboard is a community-driven tool that allows researchers and developers to submit their model results and view how they stack up against others in the field.
What metrics are used for evaluation?
The leaderboard primarily uses the COCO metric, which is the mean Average Precision (mAP) across all categories and instance sizes.
How can I submit my model results?
To submit your model, evaluate it on the COCO val 2017 dataset and follow the submission guidelines provided on the leaderboard's website.
Can I update my model's entry after submission?
Yes, you can update your model's entry by resubmitting the results. The leaderboard will reflect the latest submission for your model.