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license: apache-2.0
image: underwater laser imaging

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.

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

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