  Object detection is a crucial task for autonomous driving. In addition to
requiring high accuracy to ensure safety, object detection for autonomous
driving also requires real-time inference speed to guarantee prompt vehicle
control, as well as small model size and energy efficiency to enable embedded
system deployment.
  In this work, we propose SqueezeDet, a fully convolutional neural network for
object detection that aims to simultaneously satisfy all of the above
constraints. In our network, we use convolutional layers not only to extract
feature maps but also as the output layer to compute bounding boxes and class
probabilities. The detection pipeline of our model only contains a single
forward pass of a neural network, thus it is extremely fast. Our model is
fully-convolutional, which leads to a small model size and better energy
efficiency. While achieving the same accuracy as previous baselines, our model
is 30.4x smaller, 19.7x faster, and consumes 35.2x lower energy. The code is
open-sourced at \url{https://github.com/BichenWuUCB/squeezeDet}.
