Carly Richmond
Is it (F)ake?! Image Classification with TensorFlow.js
#1about 3 minutes
Using JavaScript and ML to solve a baking show challenge
The speaker introduces the goal of using machine learning to identify hyper-realistic cakes from the TV show "Is it Cake?".
#2about 2 minutes
Collecting and balancing the cake vs not-cake dataset
Images of cakes and non-cakes are collected using Playwright and the Unsplash API to create a balanced binary classification dataset.
#3about 5 minutes
Evaluating pre-trained models for image classification and object detection
Pre-existing models like MobileNet and Coco-SSD are tested on the dataset, but they produce inaccurate and strange classifications.
#4about 6 minutes
Building a custom convolutional neural network from scratch
A custom convolutional neural network is built using TensorFlow.js sequential models and convolution layers, but it fails to accurately classify images.
#5about 5 minutes
Applying transfer learning to improve model accuracy
Transfer learning is used by combining a pre-trained MobileNet feature vector model with a custom classification head, significantly improving results.
#6about 4 minutes
Playing an interactive game to compare human and model performance
An interactive web game allows the audience to test their cake-spotting skills against the various machine learning models.
#7about 1 minute
Key takeaways and resources for getting started with TensorFlow.js
The talk concludes by summarizing the journey from using pre-existing models to applying transfer learning and provides resources for further learning.
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