YolactEdge: Realtime Instance Segmentation on the Edge

We propose YolactEdge, the first competitive instance segmentation approach that runs on small edge devices at real-time speeds. Specifically, YolactEdge runs at up to 30.8 FPS on a Jetson AGX Xavier with a ResNet-101 backbone on 550x550 resolution images. It produces a 3-5x speed up over existing real-time methods while producing competitive mask and box detection accuracy. There are two key aspects that make our model fast and accurate on edge devices: (1) TensorRT optimization while carefully trading off speed and accuracy, and (2) a novel feature warping module to exploit temporal redundancy in videos.