Dim Sum Classifier - from Data to App part 1

Tue, 13 Aug 2019 12:00:07 +0000

Picture Credits here

In a typically machine learning lifecycle, we will need to acquire data, process data, train and validate/test models and finally deploy the trained models in applications/services. In this first part of two post, inspired by fast.ai 2019 lesson 2, we shall build a Dim Sum (a Cantonese bite-size style of cuisine with many yummy choices) classifier application by leveraging on Google Images as a data source.

Due to the wide variety of choices, we shall focus on 5 common dim sum dishes below, with links for your interest:

Rock, paper, scissors - vision transfer learning with fast.ai

Wed, 07 Aug 2019 12:00:00 +0000

Picture Credits: Wikipedia

In the previous post, we used the Rock, Paper Scissors notebook that trained a custom image classification model from scratch.

While the notebook is demonstrates building custom layers, for such a task, we can also leverage on Transfer Learning using models trained on similar image classification tasks that can often reduce time in training and experimentation and yet achieve results fairly good results, which will be shown here using the fastai v1 library as demonstrated by Jeremy Howard in his awesome Practical Deep Learning for Coders 2019 course.

Transfer-Learning on Yoke Keong

Dim Sum Classifier - from Data to App part 1

Rock, paper, scissors - vision transfer learning with fast.ai