google
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siglip2-base-patch16-naflex

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 ---
+license: apache-2.0
+tags:
+- vision
 ---
+# SigLIP 2 Base
+[SigLIP 2](https://huggingface.co/collections/google/siglip2-67b5dcef38c175486e240107)
+extends the pretraining objective of
+[SigLIP](https://huggingface.co/collections/google/siglip-659d5e62f0ae1a57ae0e83ba)
+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-base-patch16-naflex"
+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-base-patch16-naflex"
+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 [siglip2 documentation](https://huggingface.co/transformers/main/model_doc/siglip2.html#).
+## Training procedure
+SigLIP 2 adds some clever training objectives on top of SigLIP:
+1. Decoder loss
+2. Global-local and masked prediction loss
+3. Aspect ratio and resolution adaptibility
+### Training data
+SigLIP 2 is pre-trained on the WebLI dataset [(Chen et al., 2023)](https://arxiv.org/abs/2209.06794).
+### 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).
+[Evaluation Table](TODO)
+### BibTeX entry and citation info
+```bibtex
+TODO
+```