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CIS-5190-CIA/
Google_ViT

Name: Google_ViT
Availability: InStock

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Google ViT Model

Model Class

python

base_model = ViTModel.from_pretrained("google/vit-base-patch16-224-in21k")

class ViTForRegression(nn.Module):
    def __init__(self, base_model, num_outputs=2):
        super(ViTForRegression, self).__init__()
        self.base_model = base_model
        hidden_size = base_model.config.hidden_size
        self.regression_head = nn.Linear(hidden_size, num_outputs)

    def forward(self, pixel_values):
        outputs = self.base_model(pixel_values=pixel_values)
        pooler_output = outputs.pooler_output
        predictions = self.regression_head(pooler_output)
        return predictions

model = ViTForRegression(base_model).to(device)

How to Run

In the notebook ViT.ipynb, replace the line:

dataset_test = load_dataset("gydou/released_img")

with the proper location of the testing dataset.

NOTE: No .pth file, this model did not perform well enough on sample test dataset.

Training Dataset Statistics