Mirror README.md from google/siglip2-base-patch16-512@a89f5c50

encoders/google/siglip2-base-patch16-512/README.md

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+---
+license: apache-2.0
+tags:
+- vision
+widget:
+  - src: >-
+      https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/bee.jpg
+    candidate_labels: bee in the sky, bee on the flower
+    example_title: Bee
+library_name: transformers
+pipeline_tag: zero-shot-image-classification
+---
+
+# SigLIP 2 Base
+
+[SigLIP 2](https://huggingface.co/papers/2502.14786) extends the pretraining objective of
+[SigLIP](https://huggingface.co/papers/2303.15343) 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-patch16-512"
+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-patch16-512"
+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](https://huggingface.co/transformers/main/model_doc/siglip.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](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/blog/sg2-blog/eval_table.png)
+
+### 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}, 
+}
+```