Request model evaluation on COCO val 2017 dataset
Calculate memory usage for LLM models
Persian Text Embedding Benchmark
Compare LLM performance across benchmarks
Measure BERT model performance using WASM and WebGPU
Push a ML model to Hugging Face Hub
Convert Hugging Face models to OpenVINO format
Calculate VRAM requirements for LLM models
Browse and filter machine learning models by category and modality
Create and upload a Hugging Face model card
View and compare language model evaluations
Display benchmark results
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.