This repo is an official implementation for the Local InFormation Extractor (LIFE) Module framework.
 |
|---|
LIFE Module Data Flow |
- Python >= 3.6
- PyTorch >= 1.8 and torchvision
- timm:
pip install timm
Download and extract ImageNet100 train and val images from https://www.kaggle.com/datasets/ambityga/imagenet100.
The directory structure is the standard layout for the torchvision datasets.ImageFolder, and the training and validation data is expected to be in the train/ folder and val/ folder respectively:
/path/to/ImageNet100/
train/
class1/
img1.jpeg
class2/
img2.jpeg
val/
class1/
img3.jpeg
class/2
img4.jpeg
# Running training procedure with specific GPU number
./tools/run_dist_launch.sh <GPU_NUM> python main.py [optional arguments]
Example command for training DeiT Small with LIFE module with batch size of 512 on 2 GPUs
./tools/run_dist_launch.sh 2 python main.py --data-path /path/to/ImageNet100 --data-set IMNET100 --model deit_small_patch16_224_ms --batch-size 256 --input-size 224 --epochs 300 --output_dir /path/to/output/folderPlease refer to main.py for more optional arguments
# Running inference procedure with specific GPU number and model path
./tools/run_dist_launch.sh <GPU_NUM> <path_to_config> --eval --resume <model_path> [optional arguments]
Please refer to main.py for more optional arguments
Please cite the paper in your publications if it helps your research.
@article{akkaya2024enhancing,
title={Enhancing performance of vision transformers on small datasets through local inductive bias incorporation},
author={Akkaya, Ibrahim Batuhan and Kathiresan, Senthilkumar S and Arani, Elahe and Zonooz, Bahram},
journal={Pattern Recognition},
pages={110510},
year={2024},
publisher={Elsevier}
}
This code is heavily borrowed from VTPack.
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent