clip-vit-base-patch32 outputs embeddings with a fixed dimension of 512 for both images and text. This means every image embedding and every text embedding produced by the model has exactly 512 floating-point values. The fixed size is intentional and ensures that vectors from different modalities can be compared directly using similarity metrics like cosine similarity.
For developers, this fixed dimensionality simplifies system design. Storage schemas, API contracts, and indexing configurations can all be defined once and reused consistently. There is no need to handle variable-length outputs or modality-specific dimensions. Whether the input is an image or a piece of text, the output vector fits the same structure.
When storing these embeddings in a vector database such as Milvus or Zilliz Cloud, developers define a vector field with dimension 512. This predictable size helps with capacity planning, memory estimation, and index tuning. It also makes it easier to mix image and text embeddings in the same collection when building cross-modal search systems.
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