Example Concept: In an image classification model, feature embeddings are extracted by removing the final classification layer and using the output from the preceding layer. This output, often from a fully connected or pooling layer, represents the image in a lower-dimensional space, capturing its essential features. By feeding an image through the modified model, you obtain these embeddings, which can be used for tasks like similarity comparison or clustering.