| ---
|
| license: apache-2.0
|
| base_model: google/siglip-base-patch16-224
|
| tags:
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| - vision
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| - onnx
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| - 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
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| prob = sigmoid(logits)
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| ```
|
|
|
| Constants from `model_metadata.json`:
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| - `logit_scale = 4.765` (so `exp(scale) ≈ 117.33`)
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| - `logit_bias = -12.932`
|
|
|
| ## Inference
|
|
|
| ```python
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| import onnxruntime as ort
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| import numpy as np
|
|
|
| img_sess = ort.InferenceSession("siglip1_image_encoder_int8.onnx")
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| txt_sess = ort.InferenceSession("siglip1_text_encoder_int8.onnx")
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|
|
| # image: 1x3x224x224 normalized with mean/std [0.5, 0.5, 0.5]
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| image_emb = img_sess.run(None, {"pixel_values": pixel_values})[0]
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| # text: input_ids + attention_mask (use all-ones mask for canonical inference)
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| text_emb = txt_sess.run(None, {"input_ids": ids, "attention_mask": np.ones_like(ids)})[0]
|
|
|
| cos = text_emb @ image_emb.T
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| prob = 1 / (1 + np.exp(-(np.exp(4.765) * cos + -12.932)))
|
| ```
|
|
|
| ## License
|
|
|
| Apache 2.0 (inherits from base model).
|
|
|
