Vit L 16 SigLIP 384

🚀 ViT-L-16-SigLIP-384 Model Card

This is a SigLIP (Sigmoid loss for Language-Image Pre-training) model trained on WebLI. It has been converted from the original JAX checkpoints in Big Vision to PyTorch. These weights can be used in both OpenCLIP (image + text) and timm (image only).

🚀 Quick Start

The model can be used for zero-shot image classification tasks. It combines image and text features to classify images without the need for task-specific fine-tuning.

✨ Features

• Contrastive Image-Text: It can learn the relationship between images and text effectively.
• Zero-Shot Image Classification: Capable of classifying images into categories without prior training on those specific categories.

📦 Installation

The code examples assume you have the necessary libraries installed. You can install them using the following commands:

pip install torch
pip install open-clip-torch>=2.23.0 timm>=0.9.8
pip install pillow

💻 Usage Examples

Basic Usage

With OpenCLIP

import torch
import torch.nn.functional as F
from urllib.request import urlopen
from PIL import Image
from open_clip import create_model_from_pretrained, get_tokenizer # works on open-clip-torch>=2.23.0, timm>=0.9.8

model, preprocess = create_model_from_pretrained('hf-hub:timm/ViT-L-16-SigLIP-384')
tokenizer = get_tokenizer('hf-hub:timm/ViT-L-16-SigLIP-384')

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image)
    text_features = model.encode_text(text)
    image_features = F.normalize(image_features, dim=-1)
    text_features = F.normalize(text_features, dim=-1)

    text_probs = torch.sigmoid(image_features @ text_features.T * model.logit_scale.exp() + model.logit_bias)

zipped_list = list(zip(labels_list, [round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

With timm (for image embeddings)

from urllib.request import urlopen
from PIL import Image
import timm

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))

model = timm.create_model(
    'vit_large_patch16_siglip_384',
    pretrained=True,
    num_classes=0,
)
model = model.eval()

# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)

output = model(transforms(image).unsqueeze(0))  # output is (batch_size, num_features) shaped tensor

📚 Documentation

Model Details

Citation

@article{zhai2023sigmoid,
  title={Sigmoid loss for language image pre-training},
  author={Zhai, Xiaohua and Mustafa, Basil and Kolesnikov, Alexander and Beyer, Lucas},
  journal={arXiv preprint arXiv:2303.15343},
  year={2023}
}

@misc{big_vision,
  author = {Beyer, Lucas and Zhai, Xiaohua and Kolesnikov, Alexander},
  title = {Big Vision},
  year = {2022},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/google-research/big_vision}}
}

📄 License

This model is licensed under the Apache-2.0 license.