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m-toman/
siglip2-so400m-patch14-384-text

Name: siglip2-so400m-patch14-384-text
Availability: InStock

transformers

safetensors

siglip

zero-shot-image-classification

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Text tower of `google/siglip2-so400m-patch14-384`

Note: This is a stripped-down copy of google/siglip2-so400m-patch14-384 containing only the text tower. It encodes text queries into the shared embedding space and is meant to be loaded on its own at retrieval time, separately from the other tower. Splitting the towers lets you load only the half you need, saving memory and load time.

The vision tower (used at indexing time) lives in m-toman/siglip2-so400m-patch14-384-vision.

Original model card

SigLIP 2 So400m

SigLIP 2 extends the pretraining objective of SigLIP with prior, independently developed techniques into a unified recipe, for improved semantic understanding, localization, and dense features.

Intended uses

You can use the raw model for tasks like zero-shot image classification and image-text retrieval, or as a vision encoder for VLMs (and other vision tasks).

Here is how to use this model to perform zero-shot image classification:

python

from transformers import pipeline

# load pipeline
ckpt = "google/siglip2-so400m-patch14-384"
image_classifier = pipeline(model=ckpt, task="zero-shot-image-classification")

# load image and candidate labels
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
candidate_labels = ["2 cats", "a plane", "a remote"]

# run inference
outputs = image_classifier(image, candidate_labels)
print(outputs)

You can encode an image using the Vision Tower like so:

python

import torch
from transformers import AutoModel, AutoProcessor
from transformers.image_utils import load_image

# load the model and processor
ckpt = "google/siglip2-so400m-patch14-384"
model = AutoModel.from_pretrained(ckpt, device_map="auto").eval()
processor = AutoProcessor.from_pretrained(ckpt)

# load the image
image = load_image("https://huggingface.co/datasets/merve/coco/resolve/main/val2017/000000000285.jpg")
inputs = processor(images=[image], return_tensors="pt").to(model.device)

# run infernece
with torch.no_grad():
    image_embeddings = model.get_image_features(**inputs)    

print(image_embeddings.shape)

For more code examples, we refer to the siglip documentation.

Training procedure

SigLIP 2 adds some clever training objectives on top of SigLIP:

Decoder loss
Global-local and masked prediction loss
Aspect ratio and resolution adaptibility

Training data

SigLIP 2 is pre-trained on the WebLI dataset (Chen et al., 2023).

Compute

The model was trained on up to 2048 TPU-v5e chips.

Evaluation results

Evaluation of SigLIP 2 is shown below (taken from the paper).

BibTeX entry and citation info

bibtex

@misc{tschannen2025siglip2multilingualvisionlanguage,
      title={SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features}, 
      author={Michael Tschannen and Alexey Gritsenko and Xiao Wang and Muhammad Ferjad Naeem and Ibrahim Alabdulmohsin and Nikhil Parthasarathy and Talfan Evans and Lucas Beyer and Ye Xia and Basil Mustafa and Olivier Hénaff and Jeremiah Harmsen and Andreas Steiner and Xiaohua Zhai},
      year={2025},
      eprint={2502.14786},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2502.14786}, 
}

m-toman/siglip2-so400m-patch14-384-text

Text tower of google/siglip2-so400m-patch14-384