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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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--- |
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# SigLIP 2 So400m |
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[SigLIP 2](https://huggingface.co/collections/google/siglip2-67b5dcef38c175486e240107) |
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extends the pretraining objective of |
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[SigLIP](https://huggingface.co/collections/google/siglip-659d5e62f0ae1a57ae0e83ba) |
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with prior, independently developed techniques into a unified recipe, for improved semantic |
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understanding, localization, and dense features. |
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## Intended uses |
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You can use the raw model for tasks like zero-shot image classification and |
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image-text retrieval, or as a vision encoder for VLMs (and other vision tasks). |
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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 transformers import pipeline |
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# load pipeline |
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ckpt = "google/siglip2-so400m-patch14-384" |
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image_classifier = pipeline(model=ckpt, task="zero-shot-image-classification") |
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# load image and candidate labels |
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url = "http://images.cocodataset.org/val2017/000000039769.jpg" |
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candidate_labels = ["2 cats", "a plane", "a remote"] |
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# run inference |
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outputs = image_classifier(image, candidate_labels) |
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print(outputs) |
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``` |
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You can encode an image using the Vision Tower like so: |
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```python |
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import torch |
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from transformers import AutoModel, AutoProcessor |
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from transformers.image_utils import load_image |
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# load the model and processor |
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ckpt = "google/siglip2-so400m-patch14-384" |
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model = AutoModel.from_pretrained(ckpt, device_map="auto").eval() |
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processor = AutoProcessor.from_pretrained(ckpt) |
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# load the image |
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image = load_image("https://huggingface.co/datasets/merve/coco/resolve/main/val2017/000000000285.jpg") |
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inputs = processor(images=[image], return_tensors="pt").to(model.device) |
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# run infernece |
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with torch.no_grad(): |
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image_embeddings = model.get_image_features(**inputs) |
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print(image_embeddings.shape) |
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``` |
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For more code examples, we refer to the [siglip documentation](https://huggingface.co/transformers/main/model_doc/siglip.html#). |
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## Training procedure |
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SigLIP 2 adds some clever training objectives on top of SigLIP: |
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1. Decoder loss |
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2. Global-local and masked prediction loss |
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3. Aspect ratio and resolution adaptibility |
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### Training data |
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SigLIP 2 is pre-trained on the WebLI dataset [(Chen et al., 2023)](https://arxiv.org/abs/2209.06794). |
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### Compute |
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The model was trained on up to 2048 TPU-v5e chips. |
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## Evaluation results |
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Evaluation of SigLIP 2 is shown below (taken from the paper). |
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[Evaluation Table](TODO) |
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### BibTeX entry and citation info |
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```bibtex |
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TODO |
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``` |
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