---
library_name: transformers
tags:
- siglip
- siglip2
- vision
- clip
- image-embeddings
- pet-recognition
model_id: AvitoTech/SigLIP2-giant-for-animal-identification
pipeline_tag: image-feature-extraction
---

# SigLIP2-Giant Fine-tuned for Animal Identification

Fine-tuned SigLIP2-Giant 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-giant-opt-patch16-384
- **Input**: Images (384x384)
- **Output**: Image embeddings (1536-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
```

### 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 Model(nn.Module):
    def __init__(self):
        super().__init__()
        ckpt = "google/siglip2-giant-opt-patch16-384"
        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 = Model()

weights_path = hf_hub_download(repo_id="AvitoTech/SigLIP2-giant", 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()

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, 1536])
```

## 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