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[IJCV2023] Offical implementation of "SCT: A Simple Baseline for Parameter-Efficient Fine-Tuning via Salient Channels"

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Environment Setup

conda create -n SCT python=3.8
conda activate SCT
pip install -r requirements.txt

Data Preparation

1. Visual Task Adaptation Benchmark (VTAB)

  • Images

    Please refer to VTAB-source to download the datasets.

2. Few-Shot and Domain Generation

  • Images

    Please refer to DATASETS.md to download the datasets.

  • Train/Val/Test splits

    Please refer to files under data/XXX/XXX/annotations for the detail information.

Quick Start For SCT

We use the VTAB experiments as examples.

1. Downloading the Pre-trained Model

Model Link
ViT-B/16 link
ViT-L/16 link
ViT-H/14 link
Swin-B link
mkdir released_models

wget https://storage.googleapis.com/vit_models/imagenet21k/ViT-B_16.npz

wget https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_base_patch4_window7_224_22k.pth

2. Training

sh run_model_sct.sh

Acknowledgement

Part of the code is borrowed from timm.

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[IJCV2023] Offical implementation of "SCT: A Simple Baseline for Parameter-Efficient Fine-Tuning via Salient Channels"

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