Keras CNN - OOP Classification Pipeline

The project represents an attempt to build modular, OOP approach for Multi-class classification on images, which could serve as a template for other Computer Vision specific tasks.

This should be viewed as an introductory Computer Vision (CV) project, built around Convolutional Neural Network (CNN) architecture in Keras, on top of TensorFlow, version 2.3.

The project provides all abstract OOP core features and abstractions that could be used in other tasks as well. Besides this, the MNIST classification task is implemented as an example of how to use modular componentes.

Pipeline Details

The project is separated into the modules which are combined to form the following pipeline:

1. Dataset loading, batching and prefetching using 'tf.data' Dataset
2. Dataset visualisation: inspection of both original samples from the dataset, and the images after the augmentation layer is applied.
3. Model build-up: create the custom architecture specified in the configuration file. All parameters (number of ConvLayers, existence of Batch Normalization and Pooling layers, etc.) could be specified via configuration file.
4. Model training: the whole process is supported by logging tool - MLflow, so we are able to track performance across individual experiments (where each experiment is denoted with one set of the hyper-parameters).
5. Model evaluation on the test dataset: simple accuracy metric.

Set Up the Project

Install Necessary Requirements

make install

Run Pipeline

make run

MLflow Support

In order to track training procedure, the MLflow tracking would log training parameters, which could be seen on MLflow standalone server.

Run the MLflow UI from the local terminal:

mlflow ui

References

• https://github.com/The-AI-Summer/Deep-Learning-In-Production: very good materials on Deep Learning in general, including the best practices on writing Deep Learning code.