Sill-Net: Feature Augmentation with Separated Illumination Representation

This repository is the official basic implementation of Separating-Illumination Network (Sill-Net).

Usage

1. Clone the repository. The default folder name is 'Sill-Net'.

   git clone https://github.com/lanfenghuanyu/Sill-Net.git
   

2. Download the datasets used in our paper from here. The datasets used in our paper are modified from the existing datasets. Please cite the dataset papers if you use it for your research.

   • Organize the file structure as below.

   |__ Sill-Net
       |__ code
       |__ db
           |__ belga
           |__ flickr32
           |__ toplogo10
           |__ GTSRB
           |__ TT100K
           |__ exp_list
   

   • Training and test splits are defined as text files in 'Sill-Net/db/exp_list' folder.

3. Set the global repository path in 'Sill-Net/code/config.json'.

4. Run main.py to train and test the code.

Generalized one/few-shot models

Our training is based on PT-MAP, refering to the codes here. Our trained models are released here.

Training Tips

1. For better results, increase the batchsize (64 or 128). For limited GPU memory, set the batchsize as 16.

2. Adjust the number of support samples ('choose_sup' = 1 or more) for batches to balance the training speed and memory.