# Sill-Net

**Repository Path**: Lost_star/Sill-Net

## Basic Information

- **Project Name**: Sill-Net
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-08-28
- **Last Updated**: 2021-08-28

## Categories & Tags

**Categories**: Uncategorized

**Tags**: 机器学习

## README

# 中文注释版本代码
# Sill-Net: Feature Augmentation with Separated Illumination Representation

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

![image](https://github.com/lanfenghuanyu/Sill-net/blob/main/Model.png)

## 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](https://forms.gle/sytKG3QaLfgTYtau5). 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](https://github.com/yhu01/PT-MAP). Our trained models are
released [here](https://drive.google.com/drive/folders/1iQzZdFte8gcLtIZdDXASqpCgJLMnUCuP?usp=sharing).

## 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.