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--- |
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library_name: transformers |
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tags: [] |
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--- |
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# DaViT Model |
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This repository contains the implementation of the DaViT (Dual-Attention Vision Transformer) model for image classification tasks. The model leverages dual attention mechanisms to improve performance on various image datasets. |
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## Model Description |
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DaViT (Dual-Attention Vision Transformer) is designed to handle image classification tasks effectively. It combines spatial and channel attention mechanisms to capture intricate details in images. The model has multiple stages, each with convolutional embeddings and attention blocks. |
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### Example |
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Here is an example of how to use the DaViT model for image classification: |
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```python |
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import torch |
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from transformers import AutoModel, AutoConfig |
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# Load the configuration and model |
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config = AutoConfig.from_pretrained("your-username/DaViT") |
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model = AutoModel.from_pretrained("your-username/DaViT") |
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# Generate a random sample input tensor with shape (batch_size, channels, height, width) |
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batch_size = 2 |
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channels = 3 |
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height = 224 |
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width = 224 |
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sample_input = torch.randn(batch_size, channels, height, width) |
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# Pass the sample input through the model |
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output = model(sample_input) |
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# Print the output shape |
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print(f"Output shape: {output.shape}") |
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``` |
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## Files |
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- `configuration_davit.py`: Contains the `DaViTConfig` class. |
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- `modeling_davit.py`: Contains the `DaViTModel` class. |
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- `test_davit_model.py`: Script to test the model. |
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- `config.json`: Configuration file for the model. |
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- `model.safetensors`: Pretrained weights of the DaViT model. |
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## Credits |
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This model is inspired by and builds upon the ideas presented in the [Florence-2-large model by Microsoft](https://huggingface.co/microsoft/Florence-2-large). |
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