Real-time Object Detection: YOLOv1 Re-Implementation in PyTorch
This is an incremental implementation project for computer vision practitioners learning object detection pipelines.
The authors re-implemented the YOLOv1 object detection architecture in PyTorch, experimenting with modifications to the original design and comparing performance metrics against the baseline.
Real-time object detection is a crucial problem to solve when in comes to computer vision systems that needs to make appropriate decision based on detection in a timely manner. I have chosen the YOLO v1 architecture to implement it using PyTorch framework, with goal to familiarize with entire object detection pipeline I attempted different techniques to modify the original architecture to improve the results. Finally, I compare the metrics of my implementation to the original.