An implementation of SqueezeNet in chainer.
Below are some benchmarks on a ImageNet-like dataset (1 million 255x255 images with 128 batch size)
Model | Size (mb) | Parameters (million) | CPU prediction (ms) | GPU prediction (ms) | Accuracy |
---|---|---|---|---|---|
ZFNet | 117 | 16.42 | |||
SqueezeNet | 4.6 | 1.277 |
- ZFNet instead of AlexNet (I already had a trained ZFNet model). Initial filters are 7x7 instead of 11x11
- I had to add a dense layer to the end of SqueezeNet to get correct shape for my labels.
- This is the basic squeezenet without deep compression
- Paper says images are 224x224 but code and parameters suggests they use 227x227
- Seeing an 18x size reduction over ZFNet (SqueezeNet size is the same as the paper) and an 13x reduction in params.