# DINER

**Repository Path**: lizhemin15/DINER

## Basic Information

- **Project Name**: DINER
- **Description**: github访问不了
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2023-06-15
- **Last Updated**: 2023-06-15

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# DINER: Disorder-Invariant Implicit Neural Representation 

PyTorch implementation of DINER.

## Pipeline

<img src='imgs/full_res_hashmapping.png'/>

## Setup
We provide a conda environment setup file including all of the above dependencies. Create the conda environment DINER by running:
```
conda create -n diner python=3.9
conda activate diner
pip install -r requirements.txt
```
 
## Training

#### Image Representation
For tasks like fitting a single image, we prepare a test image in the `data` folder. 

To train image representations, use the config files in the `config` folder. For example, to train on the provided image, run the following
```
python train_img.py --config ./config/img.ini
```

After the image representation has been trained, the results of the image will appear in the `log/<experiment_name>` folder, where `<experiment_name>` is the subdirectory in the `log` folder corresponding to the particular training run.

#### Lensless imaging
```
python train_lensless.py --config ./config/lensless.ini
```

## Citation

```
@inproceedings{xie2023diner,
  author = {Xie, Shaowen and Zhu, Hao and Liu, Zhen and Zhang, Qi and Zhou, You and Cao, Xun and Ma, Zhan},
  title = {DINER: Disorder-Invariant Implicit Neural Representation},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={1--10},
  year = {2023}
}
```