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README.md

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+---
+library_name: transformers
+tags:
+- siglip
+- siglip2
+- vision
+- image-embeddings
+- pet-recognition
+model_id: AvitoTech/SigLIP2-Base-for-animal-identification
+pipeline_tag: image-feature-extraction
+---
+
+# SigLIP2-Base Fine-tuned for Animal Identification
+
+Fine-tuned SigLIP2-Base model for individual animal identification, specializing in distinguishing between unique cats and dogs. This model produces robust image embeddings optimized for pet recognition, re-identification, and verification tasks.
+
+
+## Model Details
+
+- **Base Model**: google/siglip2-base-patch16-224
+- **Input**: Images (224x224)
+- **Output**: Image embeddings (768-dimensional)
+- **Task**: Individual animal identification and verification
+
+## Training Data
+
+The model was trained on a comprehensive dataset combining multiple sources:
+
+- **[PetFace Dataset](https://arxiv.org/abs/2407.13555)**: Large-scale animal face dataset with 257,484 unique individuals across 13 animal families
+- **[Dogs-World](https://www.kaggle.com/datasets/lextoumbourou/dogs-world)**: Kaggle dataset for dog breed and individual identification
+- **[LCW (Labeled Cats in the Wild)](https://www.kaggle.com/datasets/dseidli/lcwlabeled-cats-in-the-wild)**: Cat identification dataset
+- **Web-scraped Data**: Additional curated images from various sources
+
+**Total Dataset Statistics:**
+- **1,904,157** total photographs
+- **695,091** unique individual animals (cats and dogs)
+
+## Training Details
+
+**Training Configuration:**
+- **Batch Size**: 116 samples (58 unique identities × 2 photos each)
+- **Optimizer**: Adam with learning rate 1e-4
+- **Training Duration**: 10 epochs
+- **Transfer Learning**: Final 5 transformer blocks unfrozen, lower layers frozen to preserve pre-trained features
+
+**Loss Function:**
+The model is trained using a combined loss function consisting of:
+1. **Triplet Loss** (margin α=0.45): Encourages separation between different animal identities
+2. **Intra-Pair Variance Regularization** (ε=0.01): Promotes consistency across multiple photos of the same animal
+
+Combined as: L_total = 1.0 × L_triplet + 0.5 × L_var
+
+This approach creates compact feature clusters for each individual animal while maintaining large separation between different identities.
+
+## Performance Metrics
+
+The model has been benchmarked against various vision encoders on multiple pet recognition datasets:
+
+### [Cat Individual Images Dataset](https://www.kaggle.com/datasets/timost1234/cat-individuals)
+
+| Model | ROC AUC | EER | Top-1 | Top-5 | Top-10 |
+|-------|---------|-----|-------|-------|--------|
+| CLIP-ViT-Base | 0.9821 | 0.0604 | 0.8359 | 0.9579 | 0.9711 |
+| DINOv2-Small | 0.9904 | 0.0422 | 0.8547 | 0.9660 | 0.9764 |
+| SigLIP-Base | 0.9899 | 0.0390 | 0.8649 | 0.9757 | 0.9842 |
+| **SigLIP2-Base** | **0.9894** | **0.0388** | **0.8660** | **0.9772** | **0.9863** |
+| Zer0int CLIP-L | 0.9881 | 0.0509 | 0.8768 | 0.9767 | 0.9845 |
+| SigLIP2-Giant | 0.9940 | 0.0344 | 0.8899 | 0.9868 | 0.9921 |
+| SigLIP2-Giant + E5-Small-v2 + gating | 0.9929 | 0.0344 | 0.8952 | 0.9872 | 0.9932 |
+
+### [DogFaceNet Dataset](https://www.springerprofessional.de/en/a-deep-learning-approach-for-dog-face-verification-and-recogniti/17094782)
+
+| Model | ROC AUC | EER | Top-1 | Top-5 | Top-10 |
+|-------|---------|-----|-------|-------|--------|
+| CLIP-ViT-Base | 0.9739 | 0.0772 | 0.4350 | 0.6417 | 0.7204 |
+| DINOv2-Small | 0.9829 | 0.0571 | 0.5581 | 0.7540 | 0.8139 |
+| SigLIP-Base | 0.9792 | 0.0606 | 0.5848 | 0.7746 | 0.8319 |
+| **SigLIP2-Base** | **0.9776** | **0.0672** | **0.5925** | **0.7856** | **0.8422** |
+| Zer0int CLIP-L | 0.9814 | 0.0625 | 0.6289 | 0.8092 | 0.8597 |
+| SigLIP2-Giant | 0.9926 | 0.0326 | 0.7475 | 0.9009 | 0.9316 |
+| SigLIP2-Giant + E5-Small-v2 + gating | 0.9920 | 0.0314 | 0.7818 | 0.9233 | 0.9482 |
+
+### Combined Test Dataset (Overall Performance)
+
+| Model | ROC AUC | EER | Top-1 | Top-5 | Top-10 |
+|-------|---------|-----|-------|-------|--------|
+| CLIP-ViT-Base | 0.9752 | 0.0729 | 0.6511 | 0.8122 | 0.8555 |
+| DINOv2-Small | 0.9848 | 0.0546 | 0.7180 | 0.8678 | 0.9009 |
+| SigLIP-Base | 0.9811 | 0.0572 | 0.7359 | 0.8831 | 0.9140 |
+| **SigLIP2-Base** | **0.9793** | **0.0631** | **0.7400** | **0.8889** | **0.9197** |
+| Zer0int CLIP-L | 0.9842 | 0.0565 | 0.7626 | 0.8994 | 0.9267 |
+| SigLIP2-Giant | 0.9912 | 0.0378 | 0.8243 | 0.9471 | 0.9641 |
+| SigLIP2-Giant + E5-Small-v2 + gating | 0.9882 | 0.0422 | 0.8428 | 0.9576 | 0.9722 |
+
+**Metrics Explanation:**
+- **ROC AUC**: Area Under the Receiver Operating Characteristic Curve - measures the model's ability to distinguish between different individuals
+- **EER**: Equal Error Rate - the error rate where false acceptance and false rejection rates are equal
+- **Top-K**: Accuracy of correct identification within the top K predictions
+
+## Basic Usage
+
+### Installation
+
+```bash
+pip install transformers torch pillow safetensors huggingface_hub
+```
+
+### Get Image Embedding
+
+```python
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from PIL import Image
+from transformers import SiglipModel, SiglipProcessor
+from safetensors.torch import load_file
+from huggingface_hub import hf_hub_download
+
+class FaceRecognizer(nn.Module):
+    def __init__(self):
+        super().__init__()
+        ckpt = "google/siglip2-base-patch16-224"
+        self.clip = SiglipModel.from_pretrained(ckpt)
+        self.processor = SiglipProcessor.from_pretrained(ckpt)
+
+    def forward(self, images):
+        clip_inputs = self.processor(images=images, return_tensors="pt").to(self.clip.device)
+        return self.clip.get_image_features(**clip_inputs)
+
+model = FaceRecognizer()
+
+weights_path = hf_hub_download(repo_id="AvitoTech/SigLIP2-Base-for-animal-identification", filename="model.safetensors")
+state_dict = load_file(weights_path)
+model.load_state_dict(state_dict)
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model = model.to(device).eval()
+
+# Get embedding
+image = Image.open("your_image.jpg").convert("RGB")
+
+with torch.no_grad():
+    embedding = model([image])
+    embedding = F.normalize(embedding, dim=1)
+
+print(f"Embedding shape: {embedding.shape}")  # torch.Size([1, 768])
+```
+
+## Citation
+
+If you use this model in your research or applications, please cite our work:
+
+```
+BibTeX citation will be added upon paper publication.
+```
+
+## Use Cases
+
+- Individual pet identification and re-identification
+- Lost and found pet matching systems
+- Veterinary record management
+- Animal behavior monitoring
+- Wildlife conservation and tracking

config.json

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+{
+  "architectures": [
+    "SiglipModel"
+  ],
+  "model_type": "siglip",
+  "projection_dim": 768,
+  "text_config": {
+    "hidden_size": 768,
+    "intermediate_size": 3072,
+    "num_attention_heads": 12,
+    "num_hidden_layers": 12,
+    "model_type": "siglip_text_model"
+  },
+  "vision_config": {
+    "hidden_size": 768,
+    "image_size": 224,
+    "intermediate_size": 3072,
+    "num_attention_heads": 12,
+    "num_channels": 3,
+    "num_hidden_layers": 12,
+    "patch_size": 16,
+    "model_type": "siglip_vision_model"
+  }
+}

model.safetensors

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+oid sha256:e03eb9324a95c217a581a322bd8e62f95a7fb7a07e4fd10e493cf297178f14a7
+size 1500802912