Of course! Let's summarize the steps in the provided code:
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Setup: The necessary libraries are imported. The possible labels for the STL10 dataset are defined, and the data directory is set.
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Data Preparation:
- Data transformations, which convert images to tensors and normalize them using a previously computed mean and standard deviation, are set up.
- The STL10 dataset's 'test' split is loaded using these transformations, and a DataLoader is created.
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Image and Label Collection: All images and labels from the DataLoader are collected into separate lists for easy access.
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Visualization & Inference:
- A function is created to plot images given their indices.
- Another function performs inference on given images using a specified model and returns predicted labels.
- Four specific images are selected and displayed.
- Inference is run on these images using two different models (a float model and a quantized model), and the results are printed out.
In summary, the code prepares and visualizes a subset of the STL10 dataset, and then it demonstrates inference on this subset using two different models, showing the predicted labels for each.