Hyperparameter	Value
Optimizer	`AdamW`
Learning Rate Schedule	`1e-4 with CosineLRScheduler`
Batch Size	`128`
Total Epochs	`20`
Early Stopping Patience	`7` on validation loss
Loss Function	CrossEntropyLoss w/ Label Smoothing (`0.1`)

Training Results

Here are the key test metrics for this model:

Test Accuracy: 0.985
Test AUC: 0.991
Test F1 Score: 0.985
Best Epoch: 15.000

How to use with `timm`

You can load this model directly from the Hugging Face Hub using timm.create_model. The config.json in this repo provides all necessary metadata.

python

import torch
import timm

# Ensure you have timm and huggingface_hub installed:
# pip install timm "huggingface_hub>=0.23.0"

# Load the model directly from the Hub
# The `pretrained=True` flag will download the weights and config automatically.
model = timm.create_model(
    'hf-hub:parlange/vit-autoscan',
    pretrained=True
)
model.eval()

# The model's default_cfg will now be populated with mean/std and input size
print(model.default_cfg)

# Example inference with a dummy input
dummy_input = torch.randn(1, 3, model.default_cfg['input_size'][-2], model.default_cfg['input_size'][-1])
with torch.no_grad():
    output = model(dummy_input)

print(f"Output shape: {output.shape}") # Should be torch.Size([1, 2])
print(f"Predictions: {torch.softmax(output, dim=1)}")

Original Checkpoint

The original .pth checkpoint file used for this model is also available in this repository.

parlange/vit-autoscan

ViT Model (vit_base_patch16_224)

Model Details

Training Hyperparameters

Training Results

How to use with timm

Original Checkpoint

parlange/
vit-autoscan

How to use with `timm`