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README.md

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+---
+license: apache-2.0
+base_model: google/siglip2-base-patch16-224
+tags:
+  - onnx
+  - vision
+  - image-text-matching
+  - nebula
+---
+
+# siglip2-base-patch16-224 (ONNX)
+
+This is [Google's SigLIP 2 base/224](https://huggingface.co/google/siglip2-base-patch16-224) exported to ONNX format for CPU inference, used by [Nebula](https://github.com/diegohh0411/nebula) for local, offline image search.
+
+## What's inside
+
+| File | Description |
+|---|---|
+| `model.onnx` | Combined vision + text encoder (~110 MB) |
+| `tokenizer.json` | SigLIP tokenizer |
+
+## Model inputs & outputs
+
+The single `model.onnx` file contains both encoders. You can run either independently by passing a dummy tensor for the unused branch.
+
+**Inputs**
+
+| Name | Shape | dtype |
+|---|---|---|
+| `pixel_values` | `[image_batch, 3, 224, 224]` | float32 |
+| `input_ids` | `[text_batch, seq_len]` | int64 |
+
+**Outputs**
+
+| Name | Shape | dtype | Description |
+|---|---|---|---|
+| `image_embeds` | `[image_batch, 768]` | float32 | L2-normalizable image embedding |
+| `text_embeds` | `[text_batch, 768]` | float32 | L2-normalizable text embedding |
+| `logits_per_image` | `[image_batch, text_batch]` | float32 | Cosine similarity scores |
+| `logits_per_text` | `[text_batch, image_batch]` | float32 | Cosine similarity scores (transposed) |
+
+## How it was exported
+
+```bash
+optimum-cli export onnx \
+  --model google/siglip2-base-patch16-224 \
+  --task zero-shot-image-classification \
+  --opset 18 \
+  ./models/
+```
+
+Requires `optimum[onnxruntime]` and `transformers`.
+
+## License
+
+Inherits [Apache 2.0](https://huggingface.co/google/siglip2-base-patch16-224) from the original Google SigLIP 2 model.