Fine-tuning an InceptionV3-based image classifier with your own dataset

Based on the Fine-tune InceptionV3 on a new set of classes example in https://keras.io/applications/

Dependencies

Very latest (>=1.0.8 from source) Keras, scipy, pillow.

Training

Structure your image files in the following directory hierarchy. Sub-sub directories are allowed and traversed:

data_dir/classname1/*.*
data_dir/classname2/*.*
...

It depends on the domain, but a few hundred images per class can already give good results.

#####Run the training:

python test_gen.py data_dir model

The standard output provides information about the state of the training, and the current accuracy. Accuracy is measured on a random 20% validation set. During training, Keras outputs the accuracy on the augmented validation dataset (val_acc). After a training round, the validation accuracy on non-augmented data is printed.

The files 000.png 001.png etc. give a visual confusion matrix about the progress of the training. 000.png is created after the newly created dense layers were trained, and the rest during fine-tuning.

The model is saved in three files, named model.h5, model.json, model-labels.json.