/Image_binary_classification_from_scratch

Build a N layer feed-forward ANN from scratch and perform image classification

Primary LanguageJupyter Notebook

Deep neural network for image binary classification

Intro

This is a project following the instruction from a deep learning online course Neural Networks and Deep Learning by Andrew Ng. We built a L layer ANN framework from scratch by implementing the forward and backward propagation as well as supportive functionalities such as image resize and crop, L2 normalization.

Datasets

The framework was applied to train two models for two different datasets:

  1. An images dataset consist of different kinds of street foods were labeled as "Hotdog" or "Not Hotdog". This dataset was inspired by the famous TV show Silicon Valley. [1] (pending)

  2. The original "Cat or Not Cat" dataset used in Andrew Ng's class.

Models

So far totally 3 examples trained for the cat/notcat dataset.

  1. A feed forward NN built from scratch. (accuracy 0.8)

  2. A feed forward NN built with pytorch. (accuracy 0.8)

  3. A CNN model with batch norm, early stop and augmentation implemented with pytorch. (accuracy 0.9)

Reference

[1] https://www.kaggle.com/dansbecker/hot-dog-not-hot-dog