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--- |
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license: apache-2.0 |
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tags: |
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- image-classification |
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- vision-transformer |
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- pytorch |
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- oxford-pets |
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library_name: torch |
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datasets: |
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- cvdl/oxford-pets |
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language: [] |
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model-index: |
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- name: ViTPets |
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results: |
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- task: |
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type: image-classification |
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dataset: |
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name: Oxford Pets |
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type: cvdl/oxford-pets |
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metrics: |
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- type: accuracy |
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value: 9 |
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--- |
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# ViTPets - Vision Transformer trained from scratch on Oxford Pets 🐶🐱 |
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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. |
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## Model Summary |
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- **Architecture**: Custom Vision Transformer (ViT) |
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- **Input resolution**: 128x128 |
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- **Patch size**: 16x16 |
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- **Embedding dimension**: 240 |
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- **Number of Transformer blocks**: 12 |
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- **Number of heads**: 4 |
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- **MLP ratio**: 2.0 |
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- **Dropout**: 10% on attention and MLP |
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- **Framework**: PyTorch |
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- **Dataset**: Oxford Pets (via 🤗 `cvdl/oxford-pets`) |
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- **Loss**: CrossEntropyLoss |
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- **Optimizer**: SGD with LR = 0.00257 |
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## Training Setup |
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- **Device**: Multi-GPU (4 GPUs) |
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- **Batch size**: 256 (64 × 4 GPUs) |
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- **Early stopping**: patience 50, delta 1e-6 |
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- **Logging**: TensorBoard |
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## How to Use |
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```python |
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from model import ViT |
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import torch |
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model = ViT( |
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img_size=(128, 128), |
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patch_size=16, |
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in_channels=3, |
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embed_dim=240, |
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n_classes=37, |
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n_blocks=12, |
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n_heads=4, |
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mlp_ratio=2.0, |
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qkv_bias=True, |
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block_drop_p=0.1, |
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attn_drop_p=0.1, |
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) |
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model.load_state_dict(torch.load("ViTPets.pth")) |
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model.eval() |
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``` |
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