parlange/deit3-autoscan

DeiT3 Model (deit3_base_patch16_224)

This repository contains a fine-tuned DeiT3 model from the timm library, intended for binary image classification.

The model weights are available in both standard PyTorch (.bin) and SafeTensors (.safetensors) formats.

Model Details

• Architecture: deit3_base_patch16_224
• Original Library: timm
• Fine-tuning Task: Binary Image Classification
• Number of Classes: 2

---

Training Hyperparameters

The model was trained with the following settings:

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: 0.990
• Test AUC: 0.995
• Test F1 Score: 0.990
• Best Epoch: 14.000

---

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.

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/deit3-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.