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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).
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