Train, Test, Re-evaluate: Schedule-Sensitive Evaluation of Generative Data for Hand Detection

1. 1Train a YOLOv8n hand detector on real and synthetic data using generative inpainting
2. 2Evaluate the detector on a real test set and a real-gloves-only test split
3. 3Fine-tune the resulting weights on real-only data at a lower learning rate
4. 4Conduct paired statistical tests to compare the performance of different training regimes
5. 5Apply multi-stage experiments to extract substantial real-deployment benefits from inpainted accessory data

Computer vision engineers and researchers working on safety-critical applications, such as occupational safety settings, can benefit from this study to improve the accuracy of hand detection models

💡 Simple multi-stage experiments can extract substantial real-deployment benefits from inpainted accessory data, improving the accuracy of hand detection models

🔍 Improve hand detection in safety-critical apps using generative inpainting & multi-stage training! 🚀