Knowledge distillation with Keras

Keras implementation of Hinton's knowledge distillation (KD), a way of transferring knowledge from a large model into a smaller model.

Summary

• I use Caltech-256 dataset for a demonstration of the technique.
• I transfer knowledge from Xception to MobileNet-0.25 and SqueezeNet v1.1.
• Results:

model	accuracy, %	top 5 accuracy, %	logloss
Xception	82.3	94.7	0.705
MobileNet-0.25	64.6	85.9	1.455
MobileNet-0.25 with KD	66.2	86.7	1.464
SqueezeNet v1.1	67.2	86.5	1.555
SqueezeNet v1.1 with KD	68.9	87.4	1.297

Implementation details

• I use pretrained on ImageNet models.
• For validation I use 20 images from each category.
• For training I use 100 images from each category.
• I use random crops and color augmentation to balance the dataset.
• I resize all images to 299x299.
• In all models I train the last two layers.

Notes on flow_from_directory

I use three slightly different versions of Keras' ImageDataGenerator.flow_from_directory:

• original version for initial training of Xception and MobileNet.
• ver1 for getting logits from Xception. Now DirectoryIterator.next also outputs image names.
• ver2 for knowledge transfer. Here DirectoryIterator.next packs logits with hard true targets. All three versions only differ in DirectoryIterator.next function.

Requirements

• Python 3.5
• Keras 2.0.6
• torchvision, Pillow
• numpy, pandas, tqdm

References

[1] Geoffrey Hinton, Oriol Vinyals, Jeff Dean, Distilling the Knowledge in a Neural Network