StreakNet-Dataset

StreakNet-Dataset is an underwater laser imaging dataset for UCLR systems. It comprises a collection of streak-tube images captured by a UCLR system at distances of 10m, 13m, 15m, and 20m. See the table below to learn more details of the dataset.

Distance	Number of streak-tube images	Resolution of streak-tube images	Data type	Training set	Validation set	Test set
10m	400	2048x2048	uint16	315,200	40,800	819,200
13m	349	2048x2048	uint16	281,992	47,530	714,752
15m	300	2048x2048	uint16	245,400	39,200	614,400
20m	267	2048x2048	uint16	229,086	31,240	546,816

Download

You can download StreakNet-Dataset for free from HuggingFace or ModelScope by Git.

Firstly, install git-lfs.

curl -s https://packagecloud.io/install/repositories/github/git-lfs/script.deb.sh | sudo bash
sudo apt update
sudo apt install git-lfs   
sudo git lfs install  --system

Then, download StreakNet-Dataset in work directory of StreakNet.

• From HuggingFace: For Global Users

cd StreakNet
git clone https://huggingface.co/datasets/Coder-AN/StreakNet-Dataset ./datasets

• From ModelScope: For Chinese Users

cd StreakNet
git clone https://www.modelscope.cn/datasets/CoderAN/StreakNet-Dataset.git ./datasets

Organizational Structure

After downloading StreakNet-Dataset from HuggingFace or ModelScope, you will see the following directory structure.

datasets
    |- clean_water_10m      # The directory of data taken at a distance of 10m
    |   |- data             # Original streak images
    |   |   |- 001.tif
    |   |   |- 002.tif
    |   |   |- 003.tif
    |   |   |- ...
    |   |
    |   |- groundtruth.npy  # The ground-truth of the final imaged image
    |   |- preview.jpg      # A preview of the ground-truth
    |
    |- clean_water_13m      # The directory of data taken at a distance of 13m (has the same structure as 10m)
    |- clean_water_15m      # The directory of data taken at a distance of 15m (has the same structure as 10m)
    |- clean_water_20m      # The directory of data taken at a distance of 20m (has the same structure as 10m)
    |- template.npy         # The 1-D time sequence of the template signal
    |- test_config.yaml     # The config file of test-set
    |- train_config.yaml    # The config file of training-set
    |- valid_config.yaml    # The config file of validation-set