  To achieve lightweight object detectors for deployment on the edge devices,
an effective model compression pipeline is proposed in this paper. The
compression pipeline consists of automatic channel pruning for the backbone,
fixed channel deletion for the branch layers and knowledge distillation for the
guidance learning. As results, the Resnet50-v1d is auto-pruned and fine-tuned
on ImageNet to attain a compact base model as the backbone of object detector.
Then, lightweight object detectors are implemented with proposed compression
pipeline. For instance, the SSD-300 with model size=16.3MB, FLOPS=2.31G, and
mAP=71.2 is created, revealing a better result than SSD-300-MobileNet.
