Request model evaluation on COCO val 2017 dataset
Convert Hugging Face models to OpenVINO format
Submit models for evaluation and view leaderboard
Evaluate LLM over-refusal rates with OR-Bench
Convert Hugging Face model repo to Safetensors
Search for model performance across languages and benchmarks
Generate leaderboard comparing DNA models
Create demo spaces for models on Hugging Face
Export Hugging Face models to ONNX
View and compare language model evaluations
Push a ML model to Hugging Face Hub
Explore and submit models using the LLM Leaderboard
Calculate GPU requirements for running LLMs
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.