A generic definition of transfer learning is that it is a deep learning technique that reuses knowledge gained from solving one problem by applying it to a related but different problem. Let's understand this by looking at an example. Let's say that we have three types of flowers—namely, a rose, a sunflower, and a tulip. We can use the standard pretrained models, such as VGG16/19, ResNet50, or InceptionV3 models (pretrained on ImageNet with 1,000 output classes, listed at https://gist.github.com/yrevar/942d3a0ac09ec9e5eb3a) to classify the flower images, but our model won't correctly classify them since these flower categories were not there in the ground-truth classes that the model was trained on. In other words, they are classes that the model is not aware of. The following image shows how the...
Python Image Processing Cookbook
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Python Image Processing Cookbook
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Overview of this book
With the advancements in wireless devices and mobile technology, there's increasing demand for people with digital image processing skills in order to extract useful information from the ever-growing volume of images. This book provides comprehensive coverage of the relevant tools and algorithms, and guides you through analysis and visualization for image processing.
With the help of over 60 cutting-edge recipes, you'll address common challenges in image processing and learn how to perform complex tasks such as object detection, image segmentation, and image reconstruction using large hybrid datasets. Dedicated sections will also take you through implementing various image enhancement and image restoration techniques, such as cartooning, gradient blending, and sparse dictionary learning. As you advance, you'll get to grips with face morphing and image segmentation techniques. With an emphasis on practical solutions, this book will help you apply deep learning techniques such as transfer learning and fine-tuning to solve real-world problems.
By the end of this book, you'll be proficient in utilizing the capabilities of the Python ecosystem to implement various image processing techniques effectively.
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Current Title:
Python Image Processing Cookbook
Table of Contents (11 chapters)
Preface
Image Manipulation and Transformation
Free Chapter
Image Enhancement
Image Restoration
Binary Image Processing
Image Registration
Image Segmentation
Image Classification
Object Detection in Images
Face Recognition, Image Captioning, and More
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