In this chapter, we will explore data labeling techniques tailored specifically for image classification, using Python. Our primary objective is to clarify the path you need to take to generate precise labels for these images in the dataset, relying on meticulously crafted rules founded upon various image properties. You will be empowered with the ability to dissect and decode images through manual inspection, harnessing the formidable Python ecosystem.
In this chapter, you will learn the following:
- How to create labeling rules based on manual inspection of image visualizations in Python
- How to create labeling rules based on the size and aspect ratio of images
- How to apply transfer learning to label image data, using pre-trained models such as YOLO V3
The overarching goal is to empower you with the ability to generate precise and reliable labels for your data. We aim to equip you with a versatile set of labeling strategies that can be applied across various machine learning projects.
We will also introduce transformations such as shearing and flipping for image labeling. We will provide you with the knowledge and techniques required to harness these transformations effectively, giving your labeling process a dynamic edge. we’ll delve into the intricacies of size, aspect ratio, bounding box, polygon annotation, and polyline annotation. You’ll learn how to derive labeling rules based on these quantitative image characteristics, providing a systematic and reliable approach to labeling data.
Technical requirements
Complete code notebooks for the examples used in this chapter are available on GitHub at https://github.com/PacktPublishing/Data-Labeling-in-Machine-Learning-with-Python.
The sample image dataset used in this chapter is available on GitHub at https://github.com/PacktPublishing/Data-Labeling-in-Machine-Learning-with-Python/tree/main/images.
Labeling rules based on image visualization
Image classification is the process of categorizing an image into one or more classes based on its content. It is a challenging task due to the high variability and complexity of images. In recent years, machine learning techniques have been applied to image classification with great success. However, machine learning models require a large amount of labeled data to train effectively.