: Unlike traditional "handcrafted" features (such as color histograms or shape descriptors) that are designed by humans, deep features are learned automatically by the model during training.

: Deep features are typically output as numerical vectors (a row of numbers) from the last fully connected or pooling layer before the final classification. Common Applications

detect simple patterns like edges, textures, or blobs. Intermediate layers combine these into more complex shapes.