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--- |
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license: apache-2.0 |
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tags: |
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- vision |
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widget: |
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- src: >- |
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https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/bee.jpg |
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candidate_labels: bee in the sky, bee on the flower |
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example_title: Bee |
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library_name: transformers |
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pipeline_tag: zero-shot-image-classification |
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--- |
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# SigLIP (shape-optimized model) |
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SigLIP model pre-trained on WebLi at resolution 224x224. It was introduced in the paper [Sigmoid Loss for Language Image Pre-Training](https://arxiv.org/abs/2303.15343) by Zhai et al. and first released in [this repository](https://github.com/google-research/big_vision). |
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This model has the SoViT-400m architecture, which is the shape-optimized version as presented in [Getting ViT in Shape: Scaling Laws for Compute-Optimal Model Design](https://arxiv.org/abs/2305.13035) by Alabdulmohsin et al. |
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Disclaimer: The team releasing SigLIP did not write a model card for this model so this model card has been written by the Hugging Face team. |
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## Model description |
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SigLIP is [CLIP](https://huggingface.co/docs/transformers/model_doc/clip), a multimodal model, with a better loss function. The sigmoid loss operates solely on image-text pairs and does not require a global view of the pairwise similarities for normalization. This allows further scaling up the batch size, while also performing better at smaller batch sizes. |
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A TLDR of SigLIP by one of the authors can be found [here](https://twitter.com/giffmana/status/1692641733459267713). |
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## Intended uses & limitations |
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You can use the raw model for tasks like zero-shot image classification and image-text retrieval. See the [model hub](https://huggingface.co/models?search=google/siglip) to look for |
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other versions on a task that interests you. |
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### How to use |
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Here is how to use this model to perform zero-shot image classification: |
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```python |
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from PIL import Image |
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import requests |
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from transformers import AutoProcessor, AutoModel |
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import torch |
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model = AutoModel.from_pretrained("google/siglip-so400m-patch14-224") |
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processor = AutoProcessor.from_pretrained("google/siglip-so400m-patch14-224") |
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url = "http://images.cocodataset.org/val2017/000000039769.jpg" |
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image = Image.open(requests.get(url, stream=True).raw) |
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texts = ["a photo of 2 cats", "a photo of 2 dogs"] |
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inputs = processor(text=texts, images=image, padding="max_length", return_tensors="pt") |
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with torch.no_grad(): |
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outputs = model(**inputs) |
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logits_per_image = outputs.logits_per_image |
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probs = torch.sigmoid(logits_per_image) # these are the probabilities |
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print(f"{probs[0][0]:.1%} that image 0 is '{texts[0]}'") |
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``` |
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Alternatively, one can leverage the pipeline API which abstracts away the complexity for the user: |
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```python |
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from transformers import pipeline |
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from PIL import Image |
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import requests |
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# load pipe |
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image_classifier = pipeline(task="zero-shot-image-classification", model="google/siglip-so400m-patch14-224") |
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# load image |
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url = 'http://images.cocodataset.org/val2017/000000039769.jpg' |
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image = Image.open(requests.get(url, stream=True).raw) |
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# inference |
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outputs = image_classifier(image, candidate_labels=["2 cats", "a plane", "a remote"]) |
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outputs = [{"score": round(output["score"], 4), "label": output["label"] } for output in outputs] |
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print(outputs) |
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``` |
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For more code examples, we refer to the [documentation](https://huggingface.co/transformers/main/model_doc/siglip.html#). |
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## Training procedure |
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### Training data |
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SigLIP is pre-trained on the WebLI dataset [(Chen et al., 2023)](https://arxiv.org/abs/2209.06794). |
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### Preprocessing |
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Images are resized/rescaled to the same resolution (384x384) and normalized across the RGB channels with mean (0.5, 0.5, 0.5) and standard deviation (0.5, 0.5, 0.5). |
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Texts are tokenized and padded to the same length (64 tokens). |
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### Compute |
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The model was trained on 16 TPU-v4 chips for three days. |
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## Evaluation results |
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Evaluation of SigLIP compared to CLIP is shown below (taken from the paper). |
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<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/siglip_table.jpeg" |
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alt="drawing" width="600"/> |
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### BibTeX entry and citation info |
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```bibtex |
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@misc{zhai2023sigmoid, |
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title={Sigmoid Loss for Language Image Pre-Training}, |
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author={Xiaohua Zhai and Basil Mustafa and Alexander Kolesnikov and Lucas Beyer}, |
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year={2023}, |
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eprint={2303.15343}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CV} |
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} |
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``` |
