Hello, after working with various object detection networks on the Jetson platform, I decided to create one with an emphasis on being easy to train and running at high FPS with low memory, aimed at hobbyist and maker projects.
Meet Keras MobileDetectNet, a network with ~300K parameters which can run at 55 FPS on the Jetson Nano using TF-TRT. It is simple to train (often producing usable results in < 50 epochs) using KITTI label format just like nVidia DIGITS and includes robust online image augmentation. Even with a small dataset of 1-2K images it manages to perform well for a network with such a small amount of parameters, perfect for hobbyist projects which need object detection. This is partially thanks to its utilization of Faster R-CNN’s anchor system, which provides much more robust bounding box regression results.
End to end source code is provided for training and inference, including how to optimize the graph with TF-TRT: https://github.com/csvance/keras-mobile-detectnet