---
license: apache-2.0
tags:
  - image-classification
  - vision-transformer
  - pytorch
  - oxford-pets
library_name: torch
datasets:
  - cvdl/oxford-pets
language: []
model-index:
  - name: ViTPets
    results:
      - task:
          type: image-classification
        dataset:
          name: Oxford Pets
          type: cvdl/oxford-pets
        metrics:
          - type: accuracy
            value: 9
---

# ViTPets - Vision Transformer trained from scratch on Oxford Pets 🐶🐱

This model is a Vision Transformer (ViT) trained from scratch on the [Oxford Pets dataset](https://huggingface.co/datasets/cvdl/oxford-pets). It classifies images of cats and dogs into 37 different breeds.

## Model Summary

- **Architecture**: Custom Vision Transformer (ViT)
- **Input resolution**: 128x128
- **Patch size**: 16x16
- **Embedding dimension**: 240
- **Number of Transformer blocks**: 12
- **Number of heads**: 4
- **MLP ratio**: 2.0
- **Dropout**: 10% on attention and MLP
- **Framework**: PyTorch
- **Dataset**: Oxford Pets (via 🤗 `cvdl/oxford-pets`)
- **Loss**: CrossEntropyLoss
- **Optimizer**: SGD with LR = 0.00257

## Training Setup

- **Device**: Multi-GPU (4 GPUs)
- **Batch size**: 256 (64 × 4 GPUs)
- **Early stopping**: patience 50, delta 1e-6
- **Logging**: TensorBoard

## How to Use

```python
from model import ViT
import torch

model = ViT(
    img_size=(128, 128),
    patch_size=16,
    in_channels=3,
    embed_dim=240,
    n_classes=37,
    n_blocks=12,
    n_heads=4,
    mlp_ratio=2.0,
    qkv_bias=True,
    block_drop_p=0.1,
    attn_drop_p=0.1,
)

model.load_state_dict(torch.load("ViTPets.pth"))
model.eval()
```