---
license: apache-2.0
base_model: google/siglip-base-patch16-224
tags:
  - vision
  - onnx
  - int8
  - mobile
  - flutter
---

# WakeUp SigLIP-1 Base INT8 (ONNX)

ONNX INT8 exports of [`google/siglip-base-patch16-224`](https://huggingface.co/google/siglip-base-patch16-224) for use in the **WakeUp** Flutter alarm app's "Travel Mode" feature.

## Files

| File | Size | Purpose |
|---|---|---|
| `siglip1_image_encoder_int8.onnx` | ~99 MB | Image feature extraction (per-scan) |
| `siglip1_text_encoder_int8.onnx` | ~111 MB | Text feature extraction (Custom Text mode only) |
| `model_metadata.json` | — | `logit_scale`, `logit_bias`, normalization params |
| `tokenizer/` | — | SigLIP-1 tokenizer files |

## Scoring

Both encoders L2-normalize their output. SigLIP scoring is:

```
logits = exp(logit_scale) * cosine(image_emb, text_emb) + logit_bias
prob   = sigmoid(logits)
```

Constants from `model_metadata.json`:
- `logit_scale = 4.765` (so `exp(scale) ≈ 117.33`)
- `logit_bias = -12.932`

## Inference

```python
import onnxruntime as ort
import numpy as np

img_sess = ort.InferenceSession("siglip1_image_encoder_int8.onnx")
txt_sess = ort.InferenceSession("siglip1_text_encoder_int8.onnx")

# image: 1x3x224x224 normalized with mean/std [0.5, 0.5, 0.5]
image_emb = img_sess.run(None, {"pixel_values": pixel_values})[0]
# text: input_ids + attention_mask (use all-ones mask for canonical inference)
text_emb = txt_sess.run(None, {"input_ids": ids, "attention_mask": np.ones_like(ids)})[0]

cos = text_emb @ image_emb.T
prob = 1 / (1 + np.exp(-(np.exp(4.765) * cos + -12.932)))
```

## License

Apache 2.0 (inherits from base model).