Image Classification with Transfer Learning and PyTorch
This article explains how to use transfer learning to classify images using PyTorch.
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Transfer learning is a technique that allows people to use a pre-trained model to create a new model. This technique can be used for image classification with PyTorch. The pre-trained model is trained on a large dataset and then the new model is trained on a smaller dataset. The pre-trained model provides a good starting point for the new model, allowing it to learn quickly and accurately.
The first step of transfer learning is to select a pre-trained model. In this article, the ResNet-18 model is used. The model is loaded into PyTorch and then the data is prepared. The data is then split into training and validation sets.
The next step is to train the model. The model is trained using a cross-entropy loss function. The model is then tested on the validation set to get an accuracy score. Finally, the model is tested on a test set to get a final accuracy score.
The article provides a step-by-step guide to using transfer learning for image classification with PyTorch. It explains the different steps involved in the process and provides code examples. This makes it easier for people to get started with transfer learning and image classification.
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