---
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).