#SqueezeNet Keras Dogs vs. Cats demo
I'm surprised SqueezeNet has generated so little hype in the Deep Learning community - it promises AlexNet accuracy with 50x fewer parameters. That is exciting, right? Let's put it to the test.
For the demo I used the Keras implementation of the Kaggle Dogs vs. Cats dataset as base and the DT42 implementation of the SqueezeNet architecture. I didn't spent much time fine tuning the hyper parameters and have intiuitively gone for a SGD optimizer with a small learning rate. As can be seen from the graph below training progressed steadily before the training accuracy seperates from the validation accuracy peaking at around +/-0.8 accuracy. Pretty impressive on a small data set of only 2000 training images.
Even more impressive though is the number of parameters and the size of the weights when saved. The Keras model I used for the demo has ~736000 parameters and only takes up 3MB of diskspace. To put in perspective compare to a VGG16 model that uses 138 million parameters and a weights file when saved to disk of over 500MB. Magnitudes smaller.
This might not mean much in a server-side world where processing power is scallable and memory cheap, but if you are looking at using Deep Learning on small form factor computers, e.g. a Raspberry Pi, this is exciting.
###References: SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and ,0.5MB model size
Dogs vs. Cats Redux: Kernels Edition
Building powerful image classification models using very little data