Pizza Clip Base v1

--- library_name: onnxruntime license: mit pipeline_tag: zero-shot-image-classification tags: - clip - onnx - onnxruntime - text-embeddings-inference - image-text-to-text - cross-modal-retrieval - pizza-engine - infinilabs --- # pizza-clip-base-v1 `pizza-clip-base-v1` is a production-oriented ONNX export of the CLIP ViT-B/32 vision and text encoders, published by Infinilabs for use with Pizza Engine and other local retrieval pipelines. It provides aligned 512-dimensional embeddings for: - image to image retrieval - text to image retrieval - image to text matching - cross-modal search demos in local or server-side inference flows ## Model origin This model family is derived from `openai/clip-vit-base-patch32` and repackaged as standalone ONNX artifacts for practical deployment with ONNX Runtime. ## Files - `clip-vit-base-patch32-vision.onnx`: vision encoder graph - `clip-vit-base-patch32-vision.onnx.data`: external vision weights - `clip-vit-base-patch32-text.onnx`: text encoder graph - `clip-vit-base-patch32-text.onnx.data`: external text weights - `tokenizer.json`: tokenizer for text encoding ## Embedding shape - dimension: 512 - image input: `pixel_values` with shape `(batch, 3, 224, 224)` - text inputs: `input_ids`, `attention_mask` Both encoders return L2-normalized embeddings so cosine similarity can be used directly for ranking. ## Intended usage This repo is intended for inference, search, and retrieval workloads, especially: - local ONNX Runtime inference - Rust-based retrieval pipelines - multimodal search prototypes - lightweight deployment without Python model loading in production ## Example use cases - retrieve poster-like images from a text query such as `historical drama poster` - rank captions against cover art - build cross-modal search in Pizza Engine ## Notes - This repository contains exported inference artifacts, not training code. - Tokenization behavior follows the upstream CLIP tokenizer. - Accuracy and behavior should track the upstream CLIP ViT-B/32 model within normal ONNX export tolerance. ## Integration Pizza Engine can use this model family for cross-modal demos alongside text embeddings such as `pizza-embed-zh-small-v1`. ## Attribution - upstream model: `openai/clip-vit-base-patch32` - packaging and ONNX deployment artifacts: Infinilabs