In this tutorial, we'll learn How to Build a Lightweight Image Classifier in TensorFlow / Keras. If only I had known about it before.
Computer vision is a rapidly developing field where tremendous progress is being made, but there are still many challenges that computer vision engineers need to tackle.
First of all, their end models need to be robust and accurate.
Secondly, the final solution should be fast enough and, ideally, achieve near real-time performance.
Lastly, the model should occupy as few computational and memory resources as possible.
Luckily for us, there are many state-of-the-art algorithms to choose from. Some are best in terms of accuracy, some are the fastest, others are incredibly compact. The arsenal is indeed very rich, and computer vision engineers have a lot of options to consider.
While computer vision has become one of the most used technologies across the globe, computer vision models are not immune to threats. . One of the reasons for this threat is the underlying lack of robustness of the models. Indrajit Kar, who is the
Computer Vision on the Edge. This article describes some of the challenges to developing and deploying CV applications, and how to mitigate them.
In this post, we'll learn Computer Vision Using TensorFlow Keras.
Starter’s pack for Computer Vision: Among the many disciplines in the field of machine learning, computer vision has arguably seen unprecedented growth.
Deep Computer Vision is capable of doing object detection and image classification task. In image classification tasks, the particular system receives some input image.