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 graphclip-vit-base-patch32-vision.onnx.data: external vision weightsclip-vit-base-patch32-text.onnx: text encoder graphclip-vit-base-patch32-text.onnx.data: external text weightstokenizer.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
