Burak Sevsay, Infrared Domain Adaptation with Zero-Shot Quantization
The quantization of neural networks is essential to meet real-time requirements. Zero-shot quantization is a key approach when training data is unavailable. To the best of our knowledge, zero-shot quantization in the infrared domain has not been explored before. This thesis examines the performance of batch normalization statistics-based zero-shot quantization on models trained with infrared imagery. We fine-tuned models pretrained on RGB images using infrared images and carefully investigated the data generation process to achieve optimal results for YOLOv8 and RetinaNet. Our results demonstrate that zero-shot quantization is more effective in the infrared domain.
Date: 03.09.2024 / 11:00 Place: B-116