README.md

---
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:

```
@Article{jimaging12010030,
AUTHOR = {Kudryavtsev, Vasiliy and Borodin, Kirill and Berezin, German and Bubenchikov, Kirill and Mkrtchian, Grach and Ryzhkov, Alexander},
TITLE = {From Visual to Multimodal: Systematic Ablation of Encoders and Fusion Strategies in Animal Identification},
JOURNAL = {Journal of Imaging},
VOLUME = {12},
YEAR = {2026},
NUMBER = {1},
ARTICLE-NUMBER = {30},
URL = {https://www.mdpi.com/2313-433X/12/1/30},
ISSN = {2313-433X},
ABSTRACT = {Automated animal identification is a practical task for reuniting lost pets with their owners, yet current systems often struggle due to limited dataset scale and reliance on unimodal visual cues. This study introduces a multimodal verification framework that enhances visual features with semantic identity priors derived from synthetic textual descriptions. We constructed a massive training corpus of 1.9 million photographs covering 695,091 unique animals to support this investigation. Through systematic ablation studies, we identified SigLIP2-Giant and E5-Small-v2 as the optimal vision and text backbones. We further evaluated fusion strategies ranging from simple concatenation to adaptive gating to determine the best method for integrating these modalities. Our proposed approach utilizes a gated fusion mechanism and achieved a Top-1 accuracy of 84.28% and an Equal Error Rate of 0.0422 on a comprehensive test protocol. These results represent an 11% improvement over leading unimodal baselines and demonstrate that integrating synthesized semantic descriptions significantly refines decision boundaries in large-scale pet re-identification.},
DOI = {10.3390/jimaging12010030}
}
```

## Use Cases

- Individual pet identification and re-identification
- Lost and found pet matching systems
- Veterinary record management
- Animal behavior monitoring
- Wildlife conservation and tracking