SQUEEZENET

Compelling Advantages

• Smaller CNNs require less communication across servers during distributed training.
• Smaller CNNs require less bandwidth to export a new model from the cloud to an autonomous car.
• Smaller CNNs are more feasible to deploy on FPGAs and other hardware with limited memory.
• SqueezeNet achieves AlexNet-level accuracy on ImageNet with 50x fewer parameters.
• More efficient distributed training
• Less overhead when exporting new models to clients

Architectural design strategies

• Replace 3x3 filters with 1x1 filters
• Decrease the number of input channels to 3x3 filters
• Downsample late in the network so that convolution layers have large activation maps

Methods

Architecture

Other squeeznet details

Experiments

Others

• early layers in the network have large strides, then most layers will have small activation maps.
• delayed downsampling to four different CNN architectures, and in each case delayed downsampling led to higher classification accuracy