# Iris_Landmarks_PyTorch

**Repository Path**: marenan_admin/Iris_Landmarks_PyTorch

## Basic Information

- **Project Name**: Iris_Landmarks_PyTorch
- **Description**: Iris landmarks localization
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-05-13
- **Last Updated**: 2021-01-15

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Dense Iris Landmarks
Repo for dense iris landmarks localization with synthesized eye dataset.

## Repo Structure
./config.py: config file

./loss.py: loss function

./checkpoint.py: save the trained model

./tools: some utitilies

./test: test results

## Prepare Dataset
There are two methods to prepare the training data.
1. You could use the software [here](https://www.cl.cam.ac.uk/research/rainbow/projects/unityeyes/tutorial.html) to synthesiz all kinds of data yourself. Then use scripts in `./gen_dataset` to generate training data.

2. You could also use the dataset I provided. directly. Just download the dataset and put train images in `./data` directory. 
   In this case the annotations are already prepared in `annotations` directory.
   
   Adress:https://pan.baidu.com/s/1gzYAVvEuhuu6L8tos3zXAQ  
   Password:990n
   
## Train
config all the training parameters in `config.py`
RUN `python training/train.py` to train your model.

Training results are kept in `results` directory.

## Test

Put well croped eye images in `./data/test`
RUN `python test/test_image.py` to test your model.

![result.jpg](https://raw.githubusercontent.com/ItchyHiker/Irsi_Landmarks/master/images/test_result.png)