deepinx / shufflenet-v2-tensorflow

a lightweight convolutional neural network

ShuffleNet v2

This is an implementation of ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design .

model	accuracy	top 5 accuracy
0.5x	0.608	0.822
1.0x	0.689	0.885

You can download trained checkpoints from here.

How to use the pretrained models

You only need two things:

1. File architecture.py. It contains a definition of the graph.
2. Checkpoint. You can load it into the graph using tf.train.Saver or tf.train.init_from_checkpoint.

For an example of using the pretrained model see: inference_with_trained_model.ipynb.

Requirements

1. for using the pretrained models: tensorflow 1.10
2. for dataset preparation: pandas, Pillow, tqdm, opencv, ...

How to train

1. Prepare ImageNet. See data/README.md.
2. Set the right parameters in the beginning of train.py file.
3. Run python train.py.

Credit

The training code is heavily inspired by:

1. https://github.com/tensorflow/models/tree/master/official/resnet
2. https://cloud.google.com/tpu/docs/inception-v3-advanced

Other implementations

1. miaow1988/ShuffleNet_V2_pytorch_caffe
2. tensorpack/examples/ImageNetModels