Pet Classifier CNN and served with BentoML

In this small project, I build a small classifier where based on the picture, it will determine if it's a dog or cat. The accuracy does not seem to be the best, but as I was mostly interested in practicing the concepts and using BentoML, that is not very important to me now. However in the near future I plan to train some other models and pick the one with the best accuracy.

Setup:

In the root of the project please create this structure:

PetImages 
  |--->Cat
  |--->Dog

and put in there the dataset.

Create test, train and validation data sets

First run python find_and_delete_broken_images.py to delete any broken images in your dataset. Run python create_test_train_validation_dir.py to create the test, train and validations datasets.

Build the model and attach it to a BentoML service:

Run python pet_classifier_model.py to build the CNN model and attach it to BentoML service.

Serve the service:

Run bentoml serve PetClassifier:latest to serve the service locally on port 5000 (Go to 127.0.0.1:5000 to see the swagger documentation and try it out.)

Credits:

1. https://github.com/abaranovskis-redsamurai/automation-repo/tree/master/convnet - source I used for the model
2. find_and_delete_broken_images.py was found somewhere in the internet, and I no longer can find the real source. Kudos to the person who wrote that piece of code. There are some slight modifications from my side there to make it cleaner.