This project implements a CNN U-Net for object detection, to compare the accuracy of randomly initialized vs pretrained nets when tasked with object detection.
The source code is based on a U-Net with final convolutional layers that downsize the U-Net feature map encoding for bounding box regression using a fixed size prior.
D B S 2024