Create README.md

README.md

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+---
+tags:
+- clip
+- siglip
+library_name: open_clip
+pipeline_tag: zero-shot-image-classification
+license: apache-2.0
+datasets:
+- webli
+---
+# Model card for ViT-B-16-SigLIP-i18n-256
+
+A SigLIP (Sigmoid loss for Language-Image Pre-training) model trained on WebLI.
+
+This model has been converted to from Open-CLIP : [timm/ViT-B-16-SigLIP-i18n-256](https://huggingface.co/timm/ViT-B-16-SigLIP-i18n-256) to huggingface CLIPVisionModel
+
+```Python
+from transformers import CLIPVisionModel, CLIPImageProcessor
+from PIL import Image
+import requests
+url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+
+image = Image.open(requests.get(url, stream=True).raw)
+inputs = image_processor(images=image, return_tensors="pt", padding=True)
+
+vision_tower = CLIPVisionModel.from_pretrained('ikala/ViT-B-16-SigLIP-i18n-256-hf')
+outputs = vision_tower(**inputs)
+
+logits_per_image = outputs.pooler_output  # this is the image-text similarity score
+```
+
+There's still a slight different where hf's CLIPVision model uses a [CLS] embedding as pool embedding while SigLIP uses global attention pooler to get the final latent feature.
+