Feature embeddings from a pre-trained ResNet model capture high-level representations crucial for One-Shot Learning comparisons.
Here is the code snippet you can refer to:

In the above code, we are using the following key points:
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Uses resnet18 as a feature extractor by removing the classification head.
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Applies standard ImageNet preprocessing for input normalization.
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Extracts and flattens a 512-dim embedding vector from input images.
Hence, leveraging ResNet's feature embeddings enables effective representation learning for One-Shot animal classification tasks.