Dim Sum Classifier – from Data to App part 2

5 min. read Picture Credits here In the previous post, we see how we can acquire data, process, clean and train an Image Classifier to identify some yummy dim sums. In this post, we shall look at completing the loop by developing the web app using starlette (a framework similar to that of flask but supports asynchronous IO), […]

Dim Sum Classifier – from Data to App part 1

7 min. read 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 […]

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

7 min. read 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 […]

Exploring Focal Length with Exiftool and R

5 min. read Picture Credits: Pixabay .main-container { max-width: 940px; margin-left: auto; margin-right: auto; } A good way to understand your shooting style and guide your future camera equipment buying decisions will be to discover your frequently used focal lengths. Focal Lengths can be extract from photos that have EXIF data, which in short refers to data on […]