Performance is important for both the training and the deployment of deep learning models. The training usually takes more time due to large data or big model architecture. The resultant models may be a bigger size and hence problematic to use in mobile devices where there is a constraint on RAM. More computing time results in more infrastructure cost. The inference time is critical in video applications. Due to the previously mentioned importance of performance, in this section, we will look at techniques to improve the performance. Reducing the model complexity is an easy option but results in decreasing accuracy. Here, we will focus on methods to improve the performance with an insignificant drop in accuracy. In the next section, we will discuss the option of quantization.
Deep Learning for Computer Vision
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Deep Learning for Computer Vision
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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.
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Current Title:
Deep Learning for Computer Vision
Table of Contents (17 chapters)
Title Page
Copyright and Credits
Packt Upsell
Foreword
Contributors
Preface
Free Chapter
Getting Started
Image Classification
Image Retrieval
Object Detection
Semantic Segmentation
Similarity Learning
Image Captioning
Generative Models
Video Classification
Deployment
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