YOLOv3-VD
Dinh Viet Sang, Duong Viet Hung · 2019
Deep neural networks (DNNs) are currently state-of-the-art methods in many important AI tasks. However, DNNs usually contain a lot of parameters that make them prone to overfitting and slow in inference. In this paper, we apply variational dropout to sparsify YOLOv3 network for vehicle detection. We then prune redundant layers and compress the network to reduce the memory size and accelerate the inference speed of the model. Experiments show that we can eliminate up to 91% weights in the original YOLOv3 with a negligible decrease of accuracy