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📢 Duality's Synthetic-to-Real Object Detection Kaggle competition is back!👏
Sign up here ➡️ ➡️ https://www.kaggle.com/competitions/multi-instance-object-detection-challenge/overview
This competition will test users' ability to train a model for multi-instance object detection. Users will:
✨Customize a cloud-based simulation
✨Output unique data for robust model training
✨Optimize training for peak model performance
Compete for cash prizes, certificates, and recognition from peer competitors around the world. Whether you’re a student, researcher, or industry pro, this challenge offers hands-on experience customizing high-fidelity synthetic data for robust models. Ready to bridge the Sim2Real gap? Join us and start building today!
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🚀 Just Found an Interesting New Leaderboard for Medical AI Evaluation!
I recently stumbled upon a medical domain-specific FACTS Grounding leaderboard on Hugging Face, and the approach to evaluating AI accuracy in medical contexts is quite impressive, so I thought I'd share.
📊 What is FACTS Grounding?
It's originally a benchmark developed by Google DeepMind that measures how well LLMs generate answers based solely on provided documents. What's cool about this medical-focused version is that it's designed to test even small open-source models.
🏥 Medical Domain Version Features
236 medical examples: Extracted from the original 860 examples
Tests small models like Qwen 3 1.7B: Great for resource-constrained environments
Uses Gemini 1.5 Flash for evaluation: Simplified to a single judge model
📈 The Evaluation Method is Pretty Neat
Grounding Score: Are all claims in the response supported by the provided document?
Quality Score: Does it properly answer the user's question?
Combined Score: Did it pass both checks?
Since medical information requires extreme accuracy, this thorough verification approach makes a lot of sense.
🔗 Check It Out Yourself
The actual leaderboard: https://huggingface.co/spaces/MaziyarPanahi/FACTS-Leaderboard
💭 My thoughts: As medical AI continues to evolve, evaluation tools like this are becoming increasingly important. The fact that it can test smaller models is particularly helpful for the open-source community!
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