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A generic image classification program that uses Google's Machine Learning library, Tensorflow and a pre-trained Deep Learning Convolutional Neural Network model called Inception.

This model has been pre-trained for the ImageNet Large Visual Recognition Challenge using the data from 2012, and it can differentiate between 1,000 different classes, like Dalmatian, dishwasher etc. The program applies Transfer Learning to this existing model and re-trains it to classify a new set of images.

Installation

Make sure you have Python installed, then install Tensorflow on your system, and clone this repo.

Usage

Prepare the image data sets

In order to start the transfer learning process, a folder named dataset needs to be created in the root of the project folder. This folder will contain the image data sets for all the subjects, for whom the classification is to be performed.

Create the dataset folder and add the images for all the data sets in the following manner:

|
---- /dataset
|    |
|    |
|    ---- /A
|    |    A1.jpg
|    |    A2.jpg
|    |    ...
|    |
|    |
|    ---- /B
|         B1.jpg
|         B2.jpg
|         ...
|

This enables classification of images between the A and B data sets.

Initiate transfer learning

Go to the project directory and run:

$ bash run.sh  

This script installs the Inception model and initiates the re-training process for the specified image data sets.

Once the process is complete, it will return a training accuracy somewhere between 85% - 100%.

The training summaries, trained graphs and trained labels will be saved in a folder named logs.

Classify objects

python classify.py image.jpg

Where image.jpg is the input file which is to be classified.

The classifier will output the predictions for each data set. A prediction score between 0.8 to 1 is considered to be optimal.

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