Below are some benchmarks on a ImageNet-like dataset (1 million 255x255 images with 128 batch size)
Model
Size (mb)
Parameters (million)
Accuracy
ZFNet
117
16.42
0.5835
SqueezeNet
4.6
1.288
0.5207
Details
ZFNet instead of AlexNet (I already had a trained ZFNet model).
This is the basic squeezenet without deep compression. I had to add a dense layer to the end of
SqueezeNet to get correct shape for my labels. I also had to add batch normalisation to the fire
modules so it would fit.
The paper says images are 224x224 but the code and parameters suggests they use 227x227. I also added some
padding, relu and initialisation that was used in the squeezenet code but not mentioned in the
paper.
Accuracy of squeezenet is lower in this test but there is a 25x size
reduction over ZFNet (SqueezeNet size is the same as in the paper) and a
12x
reduction in params.