---
library_name: transformers
tags:
- dinov2
- dino
- vision
- image-embeddings
- pet-recognition
model_id: AvitoTech/DINO-v2-small-for-animal-identification
pipeline_tag: image-feature-extraction
---

# DINOv2-Small Fine-tuned for Animal Identification

Fine-tuned DINOv2-Small 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**: facebook/dinov2-small
- **Input**: Images (224x224)
- **Output**: Image embeddings (384-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.functional as F
from PIL import Image
from transformers import AutoModel, AutoImageProcessor

# Load model and processor
processor = AutoImageProcessor.from_pretrained("facebook/dinov2-small")
model = AutoModel.from_pretrained("AvitoTech/DINO-v2-small-for-animal-identification")

device = "cuda" if torch.cuda.is_available() else "cpu"
model = model.to(device).eval()

# Load and process image
image = Image.open("your_image.jpg").convert("RGB")

with torch.no_grad():
    inputs = processor(images=[image], return_tensors="pt").to(device)
    outputs = model(**inputs)
    embedding = outputs.last_hidden_state[:, 0, :]  # CLS token
    embedding = F.normalize(embedding, dim=1)

print(f"Embedding shape: {embedding.shape}")  # torch.Size([1, 384])
```

## 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