ML Application · Python
This internship project centers on training Vision Transformer models (RT-DETRv2) for fruit object detection using YOLO-format data. The pipeline is fully modular, supports mixed precision and W&B logging, and is built with scalability in mind.
This repository provides a framework for fine-tuning Hugging Face transformer-based object detection models on custom datasets, featuring a comprehensive training loop with advanced techniques (gradient accumulation, FP16, EMA, etc.), a Streamlit inference UI, and tools for uploading models to Hugging Face Hub. It is demonstrated on a fruit object detection task with 16 models achieving mAP scores up to 0.68.
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