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Deep Learning for Computer Vision

Deep Learning for Computer Vision

By : Shanmugamani
3.2 (22)
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Deep Learning for Computer Vision

Deep Learning for Computer Vision

3.2 (22)
By: Shanmugamani

Overview of this book

Deep learning has shown its power in several application areas of Artificial Intelligence, especially in Computer Vision. Computer Vision is the science of understanding and manipulating images, and finds enormous applications in the areas of robotics, automation, and so on. This book will also show you, with practical examples, how to develop Computer Vision applications by leveraging the power of deep learning. In this book, you will learn different techniques related to object classification, object detection, image segmentation, captioning, image generation, face analysis, and more. You will also explore their applications using popular Python libraries such as TensorFlow and Keras. This book will help you master state-of-the-art, deep learning algorithms and their implementation.
Table of Contents (12 chapters)
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Exploring the datasets


The datasets available for object localization and detection are many. In this section, we will explore the datasets that are used by the research community to evaluate the algorithms. There are datasets with a varying number of objects, ranging from 20 to 200 annotated in these datasets, which makes object detection hard. Some datasets have too many objects in one image compared to other datasets with just one object per image. Next, we will see the datasets in detail.

ImageNet dataset

ImageNet has data for evaluating classification, localization, and detection tasks. The Chapter 2, Image Classification, discussed classification datasets in detail. Similar to classification data, there are 1,000 classes for localization tasks. The accuracy is calculated based on the top five detections. There will be at least one bounding box in all the images. There are 200 objects for detection problems with 470,000 images, with an average of 1.1 objects per image. 

PASCAL VOC challenge...

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