Request model evaluation on COCO val 2017 dataset
Open Persian LLM Leaderboard
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Benchmark LLMs in accuracy and translation across languages
Explain GPU usage for model training
Quantize a model for faster inference
Browse and filter machine learning models by category and modality
Generate and view leaderboard for LLM evaluations
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Submit models for evaluation and view leaderboard
Convert Stable Diffusion checkpoint to Diffusers and open a PR
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.