ResNet 50
Brett Koonce
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摘要与影响
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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学术脉络
学科主题
计算机 / AIAdvanced Neural Network Applications
Domain Adaptation and Few-Shot Learning · Machine Learning and Data Classification
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