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README.md

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+---
+license: apache-2.0
+base_model: facebook/dinov2-small
+tags:
+  - vision
+  - onnx
+  - int8
+  - mobile
+  - flutter
+  - retrieval
+  - site-recognition
+---
+
+# WakeUp DINOv2-Small INT8 (ONNX)
+
+ONNX INT8 export of [`facebook/dinov2-small`](https://huggingface.co/facebook/dinov2-small) for the **WakeUp** Flutter alarm app's "Travel Mode" → Site Recognition feature.
+
+## Why DINOv2?
+
+DINOv2 is self-supervised and explicitly optimized for **instance-level retrieval** (the same object across viewpoints / lighting). It outperforms CLIP-style models on this task by a wide margin. Used here for the Site Recognition mode where the user captures 3-5 photos of a specific object as anchors.
+
+## Files
+
+| File | Size | Purpose |
+|---|---|---|
+| `dinov2_small_int8.onnx` | ~24 MB | Image feature extraction (CLS token) |
+| `model_metadata.json` | — | Normalization params, embedding dim |
+
+## Inference
+
+```python
+import onnxruntime as ort
+
+sess = ort.InferenceSession("dinov2_small_int8.onnx")
+# image: 1x3x224x224 normalized with DINOv2 ImageNet mean/std
+embedding = sess.run(None, {"pixel_values": pixel_values})[0]  # shape (1, 384), L2-normalized
+```
+
+Anchors and scan results are compared via plain cosine similarity (dot product, since both are unit-norm).
+
+## License
+
+Apache 2.0 (inherits from base model).