Abstract
ResNet 50 is a crucial network for you to understand. It is the basis of much academic research in this field. Many different papers will compare their results to a ResNet 50 baseline, and it is valuable as a reference point. As well, we can easily download the weights for ResNet 50 networks that have been trained on the Imagenet dataset and modify the last layers (called **retraining** or **transfer learning**) to quickly produce models to tackle new problems. For most problems, this is the best approach to get started with, rather than trying to invent new networks or techniques. Building a custom dataset and scaling it up with data augmentation techniques will get you a lot further than trying to build a new architecture.
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© 2021 Brett Koonce
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Koonce, B. (2021). ResNet 50. In: Convolutional Neural Networks with Swift for Tensorflow. Apress, Berkeley, CA. https://doi.org/10.1007/978-1-4842-6168-2_6
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DOI: https://doi.org/10.1007/978-1-4842-6168-2_6
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Publisher Name: Apress, Berkeley, CA
Print ISBN: 978-1-4842-6167-5
Online ISBN: 978-1-4842-6168-2
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