flower-classifier

Image classifier using Python3 TensorFlow trained using a modified set of flower images from Kaggle.

Dependencies

The following packages are required for this project:

• pillow
• matplotlib
• tensorflow
• numpy

These dependencies can be installed via python3 -m pip install <module-name>.

Dataset

Our dataset originally comes from Kaggle, though we've removed some images that were incorrectly labeled and images that were poor examples of the flowers they were supposed to represent.

We then scaled and cropped the images using scale-and-crop-images.py so they were all 240x240px. These images can be found in the directory flowers-scaled-selected.

This second step is actually unnecessary, as TensorFlow's ImageDataGenerator will perform the image scaling automatically, though at the time we did not realize that.

InceptionV3

We're using Google's pre-trained InceptionV3 model for feature extraction. In particular, we're using the weights from InceptionV3 mentioned in the third section of Google's image classification exercise, since this project is based off of that exercise.

You can obtain a copy of the InceptionV3 weights we used with

wget https://storage.googleapis.com/mledu-datasets/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5

then

mv inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5 inception_v3_weights.h5

Using scale-and-crop-images.py

Assuming you're working with the same Kaggle dataset, extract the images to a directory such as flowers-raw/. The directory structure should be as follows:

flowers-raw/
├── daisy
├── dandelion
├── rose
├── sunflower
└── tulip

Then, run ./scale-and-crop-images.py flowers-raw/ and the script will create the directory flowers-scaled/ with the same structure as flowers-raw/, but where the images are all exactly 240x240px in size.

Using separate-images.py

This script will separate each image in the provided directory into one of three directories:

• training
• validation
• testing

Each of those directories will contain a subdirectory labeling the type of flowers in the images contained within. For example, the training directory should have the following structure:

training/
├── daisy
├── dandelion
├── rose
├── sunflower
└── tulip

Images will be randomly separated between training, testing, and validation groups in such a way that approximately 20% of images will be placed in testing, approximately 16% into validation, and the remaining approximately 64% into training.

This mirrors an 80/20 split between training and testing datasets, where the training dataset is further divided into an 80/20 split between training and validation.

Run the script with the scaled flowers directory as the argument, i.e., ./separate-images.py flowers-scaled/.

Using train-classifier.py

This script was not written to be very flexible. It requires that the InceptionV3 model be saved in the working directory as inception_v3_weights.h5.

Additionally, it requires that the flower images be separated in such a way as to have the same directory structure provided when separated by separate-images.py and that the directory be named flowers-scaled.

Finally, it saves the generated models in the directory weights which it will create if it does not exist. The saved model will be named flower-model <timestamp>.h5 where <timestamp> is in the format YYYY-MM-DD hh:mm:ss.

If using CPU TensorFlow, expect the training to take around one hour. My machine has an Intel(R) Core(TM) i7-4720HQ CPU @ 2.60GHz, and training took approximately 75 minutes per run.

Using classifier.py

This script is somewhat more flexible than train-classifier.py, though it is not as flexible as it should be.

It must be used as follows: ./classifier.py <model> <test-images> where <model> is generated by train-classifier.py and can be found as weights/flower-model YYYY-MM-DD hh:mm:ss.h5 and <test-images> is a directory containing images of flowers the model is to classify. Note that this script will not examine subdirectories in <test-images> for flower images.

The script will print to standard output the predictions it made for each image.