# Dilated-Net

**Repository Path**: summersoda/Dilated-Net

## Basic Information

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

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2024-04-21
- **Last Updated**: 2025-05-01

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Dilated-Net
The Pytorch Implementation of "Appearance-Based Gaze Estimation Using Dilated-Convolutions". (updated in 2021/04/28)


We build benchmarks for gaze estimation in our survey [**"Appearance-based Gaze Estimation With Deep Learning: A Review and Benchmark"**](https://arxiv.org/abs/2104.12668).
This is the implemented code of the "Dilated-Net" method in our benchmark. Please refer our survey for more details.

We recommend you to use **data processing codes** provided in <a href="http://phi-ai.org/GazeHub/" target="_blank">*GazeHub*</a>.
You can direct run the method' code using the processed dataset.

## Links to gaze estimation codes.

- A Coarse-to-fine Adaptive Network for Appearance-based Gaze Estimation, AAAI 2020 (Coming soon)
- [Gaze360: Physically Unconstrained Gaze Estimation in the Wild](https://github.com/yihuacheng/Gaze360), ICCV 2019
- [Appearance-Based Gaze Estimation Using Dilated-Convolutions](https://github.com/yihuacheng/Dilated-Net), ACCV 2019
- [Appearance-Based Gaze Estimation via Evaluation-Guided Asymmetric Regression](https://github.com/yihuacheng/ARE-GazeEstimation), ECCV 2018
- [RT-GENE: Real-Time Eye Gaze Estimation in Natural Environments](https://github.com/yihuacheng/RT-Gene), ECCV 2018
- [MPIIGaze: Real-World Dataset and Deep Appearance-Based Gaze Estimation](https://github.com/yihuacheng/Gaze-Net), TPAMI 2017
- [It’s written all over your face: Full-face appearance-based gaze estimation](https://github.com/yihuacheng/Full-face), CVPRW 2017
- [Eye Tracking for Everyone](https://github.com/yihuacheng/Itracker), CVPR 2016
- [Appearance-Based Gaze Estimation in the Wild](https://github.com/yihuacheng/Mnist), CVPR 2015

## Performance
The method is evaluated in three tasks. Please refer our survey for more details.
![benchmarks](benchmarkA.png)
![benchmarks](benchmarkB.png)

## License
The code is under the license of [CC BY-NC-SA 4.0 license](https://creativecommons.org/licenses/by-nc-sa/4.0/).

## Introduction
We provide two similar projects for leave-one-person-out evaluation and evaluation of common training-test split.
They have the same architecture but different started modes.

Each project contains following files/folders.
- `model.py`, the model code.
- `train.py`, the entry for training.
- `test.py`, the entry for testing.
- `config/`, this folder contains the config of the experiment in each dataset. To run our code, **you should write your own** `config.yaml`. 
- `reader/`, the data loader code. You can use the provided reader or write your own reader.

## Getting Started
### Writing your own *config.yaml*

Normally, for training, you should change 
1. `train.save.save_path`, The model is saved in the `$save_path$/checkpoint/`.
2. `train.data.image`, This is the path of image, please use the provided data processing code in <a href="http://phi-ai.org/GazeHub/" target="_blank">*GazeHub*</a>
3. `train.data.label`, This is the path of label.
4. `reader`, This indicates the used reader. It is the filename in `reader` folder, e.g., *reader/reader_mpii.py* ==> `reader: reader_mpii`.

For test, you should change 
1. `test.load.load_path`, it is usually the same as `train.save.save_path`. The test result is saved in `$load_path$/evaluation/`.
2. `test.data.image`, it is usually the same as `train.data.image`.
3. `test.data.label`, it is usually the same as `train.data.label`.
 
### Training

In the leaveout folder, you can run
```
python train.py config/config_mpii.yaml 0
```
This means the code will run with `config_mpii.yaml` and use the `0th` person as the test set.

You also can run
```
bash run.sh train.py config/config_mpii.yaml
```
This means the code will perform leave-one-person-out training automatically.   
`run.sh` performs iteration, you can change the iteration times in `run.sh` for different datasets, e.g., set the iteration times as `4` for four-fold validation.

In the traintest folder, you can run
```
python train.py config/config_mpii.yaml
```

### Test
In the leaveout folder, you can run
```
python test.py config/config_mpii.yaml 0
```
or
```
bash run.sh test.py config/config_mpii.yaml
```

In the traintest folder, you can run
```
python test.py config/config_mpii.yaml
```

### Result
After training or test, you can find the result from the `save_path` in `config_mpii.yaml`. 


## Citation
If you use our code, please cite:
```
@InProceedings{Chen_2019_ACCV,
	author="Chen, Zhaokang
	and Shi, Bertram E.",
	editor="Jawahar, C.V.
	and Li, Hongdong
	and Mori, Greg
	and Schindler, Konrad",
	title="Appearance-Based Gaze Estimation Using Dilated-Convolutions",
	booktitle="Computer Vision -- ACCV 2018",
	year="2019",
	publisher="Springer International Publishing",
	address="Cham",
	pages="309--324",
	isbn="978-3-030-20876-9"
}

@article{Cheng2021Survey,
        title={Appearance-based Gaze Estimation With Deep Learning: A Review and Benchmark},
        author={Yihua Cheng and Haofei Wang and Yiwei Bao and Feng Lu},
        journal={arXiv preprint arXiv:2104.12668},
        year={2021}
}
```

## Contact 
Please email any questions or comments to yihua_c@buaa.edu.cn.


## Reference

1. MPIIGaze: Real-World Dataset and Deep Appearance-Based Gaze Estimation
2. EYEDIAP Database: Data Description and Gaze Tracking  Evaluation Benchmarks
3. Learning-by-Synthesis for Appearance-based 3D Gaze Estimation
3. Gaze360: Physically Unconstrained Gaze Estimation in the Wild
5. ETH-XGaze: A Large Scale Dataset for Gaze Estimation under Extreme Head Pose and Gaze Variation
6. Appearance-Based Gaze Estimation in the Wild  
7. Appearance-Based Gaze Estimation Using Dilated-Convolutions
8. RT-GENE: Real-Time Eye Gaze Estimation in Natural Environments
9. It’s written all over your face: Full-face appearance-based gaze estimation
10. A Coarse-to-fine Adaptive Network for Appearance-based Gaze Estimation
11. Eye Tracking for Everyone
12. Adaptive Feature Fusion Network for Gaze Tracking in Mobile Tablets
13. On-Device Few-Shot Personalization for Real-Time Gaze Estimation
14. A Generalized and Robust Method Towards Practical Gaze Estimation on Smart Phone