Description

I implement light models with Pytorch, models are SqueezeNet, ShuffleNet, MobileNet, MobileNetv2 and ShuffleNetv2.

You can get details about these models at 纵览轻量化卷积神经网络：SqueezeNet、MobileNet、ShuffleNet、Xception

you can train the model with the command:

python main.py --model SuqeezeNet --epoch 100 --batch_size 64 --learning_rate 0.03 --use_cuda True

Limited by the computing power, I just verificate the correct of these models on CIFAR-10, don't get the best accuracy.

References

SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size

ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Inverted Residuals and Linear Bottlenecks: Mobile Networks for Classification, Detection and Segmentation

Some parts of kuangliu's code

Some parts of togheppi's code