  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 (and 172.7 FPS on an RTX 2080 Ti)
with a ResNet-101 backbone on 550x550 resolution images. To achieve this, we
make two improvements to the state-of-the-art image-based real-time method
YOLACT: (1) applying TensorRT optimization while carefully trading off speed
and accuracy, and (2) a novel feature warping module to exploit temporal
redundancy in videos. Experiments on the YouTube VIS and MS COCO datasets
demonstrate that YolactEdge produces a 3-5x speed up over existing real-time
methods while producing competitive mask and box detection accuracy. We also
conduct ablation studies to dissect our design choices and modules. Code and
models are available at https://github.com/haotian-liu/yolact_edge.
