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Model Benchmarking
Open Object Detection Leaderboard

Open Object Detection Leaderboard

Request model evaluation on COCO val 2017 dataset

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What is Open Object Detection Leaderboard ?

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.

Features

  • Model Evaluation: Supports evaluation of object detection models using standard metrics such as mAP (mean Average Precision).
  • Leaderboard Ranking: Displays models in a ranked manner based on performance metrics.
  • Configuration Flexibility: Allows for different model configurations and parameters to be tested and compared.
  • Visualizations: Provides graphs and charts to help users understand model performance at a glance.
  • Community-Driven: Open for submissions from the community, fostering collaboration and competition in object detection research.

How to use Open Object Detection Leaderboard ?

  1. Prepare Your Model: Ensure your object detection model is trained and ready for evaluation.
  2. Evaluate on COCO Val 2017 Dataset: Run your model on the COCO validation dataset (2017 version).
  3. Submit Results: Upload your model's results to the Open Object Detection Leaderboard.
  4. Check the Leaderboard: View your model's performance and compare it with other models on the leaderboard.
  5. Analyze Performance: Use the provided metrics and visualizations to identify strengths and areas for improvement.

Frequently Asked Questions

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.

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