TL;DR —
In this article, we will take a close look at two computer vision subfields: Image Segmentation and Image Super-Resolution. Two very fascinating fields. In the following article, this is a revised update on that article that I have been working on recently thanks to FastAI 18 Course. We change from inputting an image and getting a categorical output to having images as input and output. This is done by cutting and replacing the classification head with an upsampling path (this type of architectures are called fully convolutional networks)
[story continues]
Written by
@prince-canuma
Data Scientist and DevRel at Neptune.ai
Topics and
tags
tags
semantic-segmentation|deep-learning|computer-vision|fully-convolutional-network|convolutional-neural-network|image-segmentation|image-super-resolution|hackernoon-top-story
This story on HackerNoon has a decentralized backup on Sia.
Transaction ID: 5WQh-nQNs8rjBKtlCxck7A8oElfqyG_34aLJ8ZbBnW0
