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README.md

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+# SigLIP Base Patch16-256 Multilingual ONNX
+
+This directory contains ONNX exports of the `google/siglip-base-patch16-256-multilingual` model.
+
+## Model Description
+
+SigLIP (Sigmoid Loss for Language Image Pre-training) is a multimodal model similar to CLIP but with key improvements:
+
+- **Sigmoid Loss**: Uses sigmoid loss instead of contrastive loss, allowing for better scaling and performance
+- **No Global Normalization**: Operates on image-text pairs independently without requiring global batch statistics
+- **Better Multilingual Support**: Enhanced multilingual capabilities across 28+ languages
+- **Resolution**: 256x256 pixels with 16x16 patches
+
+## Directory Structure
+
+```
+siglip-base-patch16-256-multilingual-onnx/
+├── vision/
+│   ├── model.onnx          # Vision encoder
+│   ├── config.json         # Model configuration
+│   └── preprocessor_config.json
+├── text/
+│   ├── model.onnx          # Text encoder
+│   ├── config.json         # Model configuration
+│   ├── tokenizer.json      # Fast tokenizer
+│   ├── special_tokens_map.json
+│   └── spiece.model        # SentencePiece model
+└── README.md
+```
+
+## Installation
+
+```bash
+pip install onnxruntime pillow transformers
+```
+
+For GPU support:
+```bash
+pip install onnxruntime-gpu
+```
+
+## Usage
+
+### Python Example
+
+```python
+import numpy as np
+import onnxruntime as ort
+from PIL import Image
+from transformers import AutoProcessor
+
+# Load processors
+processor = AutoProcessor.from_pretrained("google/siglip-base-patch16-256-multilingual")
+
+# Load ONNX sessions
+vision_session = ort.InferenceSession("vision/model.onnx")
+text_session = ort.InferenceSession("text/model.onnx")
+
+# Process image
+image = Image.open("your_image.jpg")
+image_inputs = processor(images=image, return_tensors="np")
+image_embeddings = vision_session.run(None, {"pixel_values": image_inputs["pixel_values"]})[0]
+
+# Process text
+texts = ["a photo of a cat", "une photo d'un chat", "una foto de un gato"]
+text_inputs = processor(text=texts, padding=True, return_tensors="np")
+text_embeddings = text_session.run(None, {
+    "input_ids": text_inputs["input_ids"],
+    "attention_mask": text_inputs["attention_mask"]
+})[0]
+
+# Compute similarity using sigmoid (not softmax like CLIP!)
+# SigLIP uses sigmoid activation, so we compute sigmoid of the dot product
+logits = np.dot(image_embeddings, text_embeddings.T)
+probs = 1 / (1 + np.exp(-logits))  # sigmoid activation
+
+print("Probabilities:")
+for i, text in enumerate(texts):
+    print(f"  {text}: {probs[0][i]:.2%}")
+```
+
+## Key Differences from CLIP
+
+1. **Activation Function**: SigLIP uses sigmoid instead of softmax
+2. **Loss Function**: Sigmoid loss instead of contrastive loss
+3. **No L2 Normalization**: Embeddings are not L2-normalized
+4. **Independent Pairs**: Each image-text pair is processed independently
+
+## Performance
+
+- **Inference Speed**: ~2-3x faster than PyTorch model
+- **Memory Usage**: ~50% reduction compared to PyTorch
+- **Accuracy**: Identical outputs to original model (within floating-point precision)
+
+## Supported Languages
+
+Arabic (ar), Bengali (bn), German (de), Greek (el), English (en), Spanish (es), 
+Finnish (fi), French (fr), Hebrew (he), Hindi (hi), Indonesian (id), Italian (it), 
+Japanese (ja), Korean (ko), Dutch (nl), Norwegian (no), Polish (pl), Portuguese (pt), 
+Romanian (ro), Russian (ru), Swedish (sv), Swahili (sw), Tamil (ta), Thai (th), 
+Turkish (tr), Ukrainian (uk), Vietnamese (vi), Chinese (zh)
+
+## Model Details
+
+- **Vision Encoder**: ViT-Base with 256x256 input, 16x16 patches
+- **Text Encoder**: Transformer-based with SentencePiece tokenizer
+- **Embedding Dimension**: 768
+- **ONNX Opset**: 14
+- **Precision**: FP32
+
+## Citation
+
+```bibtex
+@article{zhai2023sigmoid,
+  title={Sigmoid Loss for Language Image Pre-Training},
+  author={Zhai, Xiaohua and others},
+  journal={arXiv preprint arXiv:2303.12170},
+  year={2023}
+}
+```
+
+## License
+
+Please refer to the original model's license at: https://huggingface.co/google/siglip-base-patch16-256-multilingual

text/config.json

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+{
+  "model_type": "siglip_text",
+  "hidden_size": 768,
+  "max_seq_length": 64,
+  "vocab_size": 250000,
+  "model_name": "google/siglip-base-patch16-256-multilingual",
+  "onnx_export_version": "1.0",
+  "tokenizer_type": "sentencepiece",
+  "languages": [
+    "ar",
+    "bn",
+    "de",
+    "el",
+    "en",
+    "es",
+    "fi",
+    "fr",
+    "he",
+    "hi",
+    "id",
+    "it",
+    "ja",
+    "ko",
+    "nl",
+    "no",
+    "pl",
+    "pt",
+    "ro",
+    "ru",
+    "sv",
+    "sw",
+    "ta",
+    "th",
+    "tr",
+    "uk",
+    "vi",
+    "zh"
+  ]
+}

text/model.onnx

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text/special_tokens_map.json

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+    "content": "</s>",
+    "lstrip": true,
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+    "rstrip": true,
+    "single_word": false
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+  "pad_token": {
+    "content": "</s>",
+    "lstrip": true,
+    "normalized": false,
+    "rstrip": true,
+    "single_word": false
+  },
+  "unk_token": {
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+    "lstrip": true,
+    "normalized": false,
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+  }
+}

text/spiece.model

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text/tokenizer_config.json

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+  "clean_up_tokenization_spaces": true,
+  "do_lower_case": true,
+  "eos_token": "</s>",
+  "extra_special_tokens": {},
+  "model_input_names": [
+    "input_ids"
+  ],
+  "model_max_length": 64,
+  "pad_token": "</s>",
+  "processor_class": "SiglipProcessor",
+  "sp_model_kwargs": {},
+  "tokenizer_class": "SiglipTokenizer",
+  "unk_token": "<unk>"
+}

vision/config.json

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+{
+  "model_type": "siglip_vision",
+  "hidden_size": 768,
+  "image_size": 256,
+  "patch_size": 16,
+  "num_channels": 3,
+  "model_name": "google/siglip-base-patch16-256-multilingual",
+  "onnx_export_version": "1.0"
+}

vision/model.onnx

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vision/preprocessor_config.json

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+{
+  "do_convert_rgb": null,
+  "do_normalize": true,
+  "do_rescale": true,
+  "do_resize": true,
+  "image_mean": [
+    0.5,
+    0.5,
+    0.5
+  ],
+  "image_processor_type": "SiglipImageProcessor",
+  "image_std": [
+    0.5,
+    0.5,
+    0.5
+  ],
+  "processor_class": "SiglipProcessor",
+  "resample": 3,
+  "rescale_factor": 0.00392156862745098,
+  "size": {
+    "height": 256,
+    "width": 256
+  }
+}

vision/special_tokens_map.json

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+  "do_lower_case": true,
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+  "model_input_names": [
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+  "model_max_length": 64,
+  "pad_token": "</s>",
+  "processor_class": "SiglipProcessor",
+  "sp_model_kwargs": {},
+  "tokenizer_class": "SiglipTokenizer",
+  "unk_token": "<unk>"
+}