README.md

deepClassificationTool

Deep Image Classification Tool based on Keras. Tool implements light versions of VGG, ResNet and InceptionV3 for small images. Tool uses python 3.5.

Tool has 3 modes:

1. Training of new deep neural network (train_flag = True, tune_flag = False).
2. Tuning of existing deep neural network (train_flag = True, tune_flag = True).
3. Testing of existing (trained) deep neural network (train_flag = False, tune_flag = False) or (train_flag = False, tune_flag = True).

For training and tune mode you need two folders:

1. Training folder with subfolders - one for each image class
2. Test folder with subfolders - one for each image class

Example of folders tree:

 train/ImageClass1
      /ImageClass2
      /ImageClass3
      /ImageClass4
      ...

test/ImageClass1 
    /ImageClass2
    /ImageClass3
    /ImageClass4
    ...

Here each subfolder 'ImageClassi' consists set of images of i-th class.

Description of main modules:

deepClassificationTool.py - main module for training and testing of deep neural network

testFunctions.py - functions for testing of trained deep neural network on one image, two images and folder of images (with calculation of recall, precision for each class and accuracy)

modelVGGm.py - Light version of VGG for small images - Inspired from VGG, 2014 - VGGm(modified)

modelResNetM.py - Light version of ResNet for small images - resNetM (modified)

                # Reference - [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385)
                
                # Reference - [https://github.com/fchollet/keras/blob/master/keras/applications/resnet50.py]

modelInceptionV3m.py - Light version of inceptionV3 - inceptionV3m (modified)

                # Reference - [https://github.com/fchollet/keras/blob/master/keras/applications/inception_v3.py]
                
                # Reference - [Rethinking the Inception Architecture for Computer Vision](http://arxiv.org/abs/1512.00567)