Summary
In this chapter, we embarked on an enlightening journey into the world of image labeling and classification. We began by mastering the art of creating labeling rules through manual inspection, tapping into the extensive capabilities of Python. This newfound skill empowers us to translate visual intuition into valuable data, a crucial asset in the realm of machine learning.
As we delved deeper, we explored the intricacies of size, aspect ratio, bounding boxes, and polygon and polyline annotations. We learned how to craft labeling rules based on these quantitative image characteristics, ushering in a systematic and dependable approach to data labeling.
Our exploration extended to the transformative realm of image manipulation. We harnessed the potential of image transformations such as shearing and flipping, enhancing our labeling process with dynamic versatility.
Furthermore, we applied our knowledge to real-world scenarios, classifying plant disease images using rule...
