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cat-dog-cnn-classifier

Description

The dataset used on this classification model comes from a competition that aimed to develop an image classifier trained from images with dogs and cats. This dataset can be accessed clicking in the following link: Kaggle Cats and Dogs Dataset

Features

The attributes on this dataset are the information contained on every single image as an array of pixels [Black:0 | White:255]. Every array has the following shape: [image_width, image_height, channel]. Originally, the images contain 3 channels, one channel for every color (RGB).

Classes
  • class 1 : dog
  • class 2 : cat

Dependencies

You can use pip or conda to install the dependencies:

  • tensorflow
  • matplotlib
  • jupyter
  • pandas
  • pillow
  • scikit-learn
  • imageio
  • OpenCV

Usage

If you want to try this program, download this repo and launch jupyter to run it on your machine.

- - - TODO - - -

  • ENVIRONMENT PREPARATION

    • Install library dependencies
    • Document installation and usage

  • DATA EXPLORATION

    • Add dataset description
    • Preview the structure of the dataset
    • Add data visualizations

  • DATA PREPROCESSING

    • Apply standarization to feature data
    • Apply one-hot encoding to categorical data
    • Split data into training and testing sets
    • Output preprocessed data for faster preloading

  • DATA ANALYSIS

    • Define network parameters
    • Define network structure
    • Add different network configurations
      • Define learning rate with different decaying methods
      • Set up cost, optimizer, and accuracy function with different configurations
    • Define model execution
    • Visualize evolution of MSE on training and testing datasets through epoch iteration
    • Visualize evolution of loss function
    • Visualize evolution of learning rate
    • Add log and summary writer
    • Add Tensorboard visualization
    • Add checkpoints for model restoration

  • MODEL DEPLOYMENT

    • Load a pretrained model
    • Test it with new data

  • OTHERS

    • Update README files
    • Update all nbviewer links
    • Add Tensorflow 1.x, Tensorflow 2.x, keras, tf.keras, and scikit-learn data analysis notebooks