Offered By: IBM
Waste Classification: Hands-on Guide to Transfer Learning
In this Guided Project, you will use transfer learning and fine-tuning to categorize waste streams based on disposal options: organic or recyclable.Continue reading
Deep Learning176 Enrolled
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.
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.
Frequently Asked Questions
Skills You Will Learn
Python, Data Science, Machine Learning, Deep Learning
Data Scientist Intern at IBM
I am a Data Scientist Intern at IBM, and a Masters student in Computer Science at the University of Toronto. I am passionate about building data science, and machine learning-based systems for improving various aspects of life.Read more
Data Scientist at IBM
I am an aspiring Data Scientist at IBM with extensive theoretical/academic, research, and work experience in different areas of Machine Learning, including Classification, Clustering, Computer Vision, NLP, and Generative AI. I've exploited Machine Learning to build data products for the P&C insurance industry in the past. I also recently became an instructor of the Unsupervised Machine Learning course by IBM on Coursera!Read more
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