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https://github.com/showlab/SCT
[IJCV2023] Offical implementation of "SCT: A Simple Baseline for Parameter-Efficient Fine-Tuning via Salient Channels"
https://github.com/showlab/SCT
Last synced: 9 days ago
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[IJCV2023] Offical implementation of "SCT: A Simple Baseline for Parameter-Efficient Fine-Tuning via Salient Channels"
- Host: GitHub
- URL: https://github.com/showlab/SCT
- Owner: showlab
- Created: 2023-09-18T12:40:48.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2023-09-18T12:40:57.000Z (about 1 year ago)
- Last Synced: 2024-08-02T15:30:44.019Z (3 months ago)
- Language: Python
- Homepage:
- Size: 110 KB
- Stars: 10
- Watchers: 2
- Forks: 0
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
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README
## 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](https://github.com/ZhangYuanhan-AI/NOAH/tree/main/data/vtab-source) to download the datasets.
### 2. Few-Shot and Domain Generation
- Images
Please refer to [DATASETS.md](https://github.com/KaiyangZhou/CoOp/blob/main/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](https://storage.googleapis.com/vit_models/imagenet21k/ViT-B_16.npz)|
|ViT-L/16 | [link](https://storage.googleapis.com/vit_models/imagenet21k/ViT-L_16.npz)|
|ViT-H/14 | [link](https://storage.googleapis.com/vit_models/imagenet21k/ViT-H_14.npz)|
|Swin-B | [link](https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_base_patch4_window7_224_22k.pth)|
```
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](https://github.com/rwightman/pytorch-image-models).