Tensorflow Image Classification

Updated to Tensorflow 1.2

This repo demonstrates an image classification system for any set of images reusably. It uses tensorflow to create a neural network and do the image classification, and imagemagick to preprocess images. It then will generate data files for the images and labels for use within tensorflow.

Prerequisites

Make sure you have:

• Tensorflow
• Imagemagick
• Wand (python imagemagick wrapper)
• Set of pre-classified images (both a training set and a test set)

How It Works

1. Put images into data/input/, seperating them into different directories for training data and test data (data/input/training and data/input/test for example)
2. Further seperate images into directories representing different classifications (data/input/[training/test]/class1, data/input/[training/test]/class2 etc...)
3. Modify the trainingset.json and testset.json to set the height and width of images, as well as adding the classification directories made in (2), giving them a label. The json files have an example already written in, assuming 2 classifications in directories data/input/[training/test]/positive and data/input/[training/test]/negative for a binary classification (if output files are modified, their location will also need to be modified in tf_data.py
4. Run ./build_data_set [dataset].json which will build the imageset and labelset into the outputfiles specified in the json file (output/TF[test/train].[labels & images].gz by default)
5. Note - in tf_data.py you will need to set the number of classes you have defined in the function read_data_sets
6. Run python classify.py to train the model and test the data set.