saft 1.2.0

SAFT: Self-Attention Factor-Tuning for Parameter-Efficient Fine-Tuning

SAFT: Self-Attention Factor-Tuning

A highly efficient fine-tuning technique for large-scale neural networks.

Table of Contents

• Quickstart
• VTAB-1k Test
• Pretrained Model
• Results

Quickstart

Easily install SAFT using pip and get started with a simple example.

Installation

pip install saft

Example Usage

from saft.saft import saft

if __name__ == "__main__":
    saft_instance = saft(
        model='vit_base_patch16_224',
        num_classes=get_classes_num('oxford_flowers102'),
        validation_interval=1,
        rank=3,
        scale=10
    )
    # Replace with your PyTorch DataLoader objects
    # train_dl, test_dl = [your data in a pytorch dataloader]
    # saft_instance.upload_data(train_dl, test_dl)
    
    saft_instance.train(10)
    trained_model = saft_instance.model

VTAB-1k Test

To run tests on the VTAB-1K dataset, follow these steps:

1. Visit the SSF Data Preparation page to download the VTAB-1K dataset.
2. Place the downloaded dataset folders in <YOUR PATH>/SAFT/data/.

Pretrained Model

For a quick start, download the pretrained ViT-B/16 model:

• Download ViT-B/16
• Place the downloaded model in <YOUR PATH>/SAFT/ViT-B_16.npz.

Results

Achieve remarkable performance with only 0.055 million trainable backbone parameters using ViT-B/16.