Carly Richmond
Mastering Image Classification: A Journey with Cakes
#1about 4 minutes
The origin of the "Is it Cake?" machine learning project
Inspired by the TV show "Is it Cake?", a personal project was started to build an image classifier using TensorFlow.js.
#2about 3 minutes
Sourcing and preparing the cake and not-cake image data
Data was collected by scraping bakers' websites with Playwright and using the Unsplash API, but this introduced data quality issues like logos and ambiguous images.
#3about 3 minutes
Evaluating the pre-trained MobileNet image classification model
The pre-trained MobileNet model in TensorFlow.js was tested but performed poorly, often misclassifying cakes as candles or bakeries.
#4about 2 minutes
Using the Coco-SSD model for object detection
The Coco-SSD object detection model performed better than MobileNet but still made significant errors, like identifying a cake as a person.
#5about 6 minutes
Building a custom convolutional neural network from scratch
An attempt to build a custom Convolutional Neural Network (CNN) using TensorFlow.js sequential models resulted in failure, with the model classifying every image as "not cake".
#6about 2 minutes
Improving model accuracy with transfer learning
Transfer learning was used by taking the feature extraction layers of MobileNet and adding a custom classification head, which significantly improved the model's performance.
#7about 4 minutes
Playing the "Is it Cake?" game and comparing results
The audience participates in an interactive game to classify images, outperforming the custom models and demonstrating the difficulty of the task.
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