Siglip2 Large Patch16 384

Files

The text-tower's external-data blob (siglip2-large-patch16-384_text.onnx_data) must sit next to the .onnx file when loading; onnx.load(...) and onnxruntime.InferenceSession(...) resolve the relative reference automatically.

Usage

import onnxruntime as ort
from tokenizers import Tokenizer

# Image tower
img_sess = ort.InferenceSession("siglip2-large-patch16-384_image.onnx",
                                providers=["CUDAExecutionProvider", "CPUExecutionProvider"])
img_emb = img_sess.run(None, {"pixel_values": chw_image_batch})[0]

# Text tower
tok = Tokenizer.from_file("siglip2-large-patch16-384.json")
ids = [tok.encode(t).ids[:64] for t in texts]
txt_sess = ort.InferenceSession("siglip2-large-patch16-384_text.onnx",
                                providers=["CUDAExecutionProvider", "CPUExecutionProvider"])
txt_emb = txt_sess.run(None, {"input_ids": ids})[0]

Source license

Inherits Apache-2.0 from the upstream model.

siglip2-large-patch16-384_text.onnx

onnx · 407 KB · SHA-256 f8daaf47a06f…b354 · Hugging Face

--- license: apache-2.0 library_name: onnx tags: - siglip - siglip2 - image-text - onnx pipeline_tag: zero-shot-image-classification --- # siglip2-large-patch16-384 (ONNX) ONNX export of [`google/siglip2-large-patch16-384`](https://huggingface.co/google/siglip2-large-patch16-384) — the image and text towers as two separate ONNX files, sharing a tokenizer. - **Source model:** `google/siglip2-large-patch16-384` - **Image input:** `NCHW`, 384×384, float32, SigLIP normalization - **Text input:** fixed-length 64 tokens (SigLIP2's training-time max) - **Embedding dim:** **1024** (both towers, joint embedding space) ## Files | File | Purpose | Size | | ---- | ------- | ---- | | `siglip2-large-patch16-384_image.onnx` | image tower (self-contained) | 1.2 GB | | `siglip2-large-patch16-384_text.onnx` + `_data` | text tower (one external-data blob) | 0.4 MB + 2.1 GB | | `siglip2-large-patch16-384.json` | fast-tokenizer artifact | 33 MB | The text-tower's external-data blob (`siglip2-large-patch16-384_text.onnx_data`) must sit next to the `.onnx` file when loading; `onnx.load(...)` and `onnxruntime.InferenceSession(...)` resolve the relative reference automatically. ## Usage ```python import onnxruntime as ort from tokenizers import Tokenizer # Image tower img_sess = ort.InferenceSession("siglip2-large-patch16-384_image.onnx", providers=["CUDAExecutionProvider", "CPUExecutionProvider"]) img_emb = img_sess.run(None, {"pixel_values": chw_image_batch})[0] # Text tower tok = Tokenizer.from_file("siglip2-large-patch16-384.json") ids = [tok.encode(t).ids[:64] for t in texts] txt_sess = ort.InferenceSession("siglip2-large-patch16-384_text.onnx", providers=["CUDAExecutionProvider", "CPUExecutionProvider"]) txt_emb = txt_sess.run(None, {"input_ids": ids})[0] ``` ## Source license Inherits [Apache-2.0 from the upstream model](https://huggingface.co/google/siglip2-large-patch16-384).