Implementation of SqueezeNet by chainer
git clone https://github.com/nutszebra/squeeze_net.git
cd squeeze_net
git submodule init
git submodule update
python main.py -p ./ -e 300 -b 64 -g 0 -s 1 -trb 1 -teb 1 -lr 0.1
My squeezenet is with simple bypass and most network parameters are same as in [1]. However, the implementation slightly differs from the original implemenatation [1].
- Fire module
As [2] is reported, the order of BN_ReLU_Conv works well for residual networks, thus Fire module is composed of three BN_ReLU_Conv layers. - Optimization
Optimization and hyperparameters are same as in [3]. - Data augmentation
Train: Pictures are resized in the range of [124, 132], then 122x122 patches are extracted randomly and are normalized locally. Horizontal flipping is applied with 0.5 probability.
Test: Pictures are randomly resized to 128x128, then they are normalized locally. Single image test is used to calculate total accuracy.
| network | total accuracy (%) |
|---|---|
| Alexnet without data augmentation[4] | 82 |
| Squeezenet [1] | 92.63 |
SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size [1]
Identity Mappings in Deep Residual Networks [2]
Densely Connected Convolutional Networks [3]
Alexnet without data augmentation [4]