Waste Classification: Hands-on Guide to Transfer Learning
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Learn to classify waste using transfer learning techniques. Understand how to leverage pre-trained models for efficient waste classification tasks using image data, and build a robust waste management system using machine learning.
At a Glance
In this Guided Project, you will use transfer learning and fine-tuning to categorize waste streams based on disposal options: organic or recyclable.
You are a data science intern at a waste management service. Your manager has asked you to create a waste classification pipeline that categorizes waste streams based on disposal options: organic or recyclable. What type of model should you use?
Most popular models are difficult to train from scratch as they require huge datasets (like ImageNet), a large number of training iterations, and very heavy computing machinery. The basic features (edges, shapes) learned by early layers in a network are generalizable. While the later layers in an already trained network tend to capture features that are more particular to a specific image classification task.
- Initialize base model, and load pre-trained weights (e.g. ImageNet)
- “Freeze” layers in the base model by setting training = False
- Define a new model that goes on top of the output of the base model’s layers.
- Train the resulting model on your data set.
In this lab, you’re going to train a transfer learning model to perform this image classification task.
A Look at the Project Ahead
After completing this guided project you will be able to:
- Perform pre-processing and image augmentation on ImageGeneratorClass objects in Keras.
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Implement transfer learning in five general steps:
- obtain a pre-trained model,
- create base model,
- freeze layers,
- train new layers on dataset,
- improve model through fine-tuning.
- Build an end-to-end VGG16-based transfer learning model for binary image classification tasks.
This course mainly uses Python and JupyterLabs. Although these skills are recommended prerequisites, no prior experience is required as this Guided Project is designed for complete beginners.
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