Classifying Cats & Dogs with HOG and SVM
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Understand how to classify images of cats and dogs using Histogram of Oriented Gradients (HOG) features and Support Vector Machines (SVM). Learn the basics of image classification and machine learning.
At a Glance
Classify images of cats and dogs by extracting Histogram of Oriented Gradients features from them for a Support Vector Machine model! In particular, you will be able to feed an image to the model yourself and get a prediction.
How do we tell apart cats and dogs? As humans, this is an easy task. Dogs don’t have claws as sharp as cats. Dogs are social animals whereas cats
A Look at the Project Ahead
After completing this guided project you will be able to:
- Extract H.O.G. features from images
- Train an SVM model on image inputs
- Tune hyperparameters with Grid Search and evaluate model performance
- Classify new images of cats and dogs with SVM
What You’ll Need
This course mainly uses Python, specifically the OpenCV, sklearn, and numpy libraries. Although these skills are recommended prerequisites, no prior experience is required as this Guided Project is designed for complete beginners.
Frequently Asked Questions
Your Instructor
Aije is a Data Scientist at IBM who holds a degree in Economics and Statistics from the University of Manitoba and a Post-grad in Business Analytics from St. Lawrence College, Kingston. She is currently pursuing her Masters in Management Analytics at Queens University. She is part of the IBM Developer Skills Network group where she brings her real-world experience to the courses she creates.
Cindy Huang
Cindy is a data science associate of the Skills Network team. She has a passion for machine learning to improve user experience, especially in the area of computational linguistics.
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