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Database of snow holograms collected from 2019 to 2022 for machine learning training or other purposes

Li, Jiaqi
Guala, Michele
Hong, Jiarong
2022-10-06
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Persistent link to this item

https://doi.org/10.13020/7akr-8n50
 https://hdl.handle.net/11299/241878
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Collection period

2018-01-14
2022-04-17

Date completed

2022-07-01

Date updated

Time period coverage

Geographic coverage

Source information

Journal Title

Journal ISSN

Volume Title

Title

Database of snow holograms collected from 2019 to 2022 for machine learning training or other purposes

Published Date

2022-10-06

Authors

Group

University of Minnesota Flow Field Imaging Lab

Author Contact

Li, Jiaqi
li001334@umn.edu

Type

Dataset
3D Imaging Data
Experimental Data
Field Study Data

Abstract

This dataset includes the original combined snow holograms and holograms with image augmentation (rotation, exposure, blur, noise) for YOLOv5 model training to detect and classify snow particles. The individual snow particles are cropped and combined to enrich the particle numbers in each image for the ease of manual labeling. The snow particles are classified into six categories, including aggregate/irregular (I), dendrite (P2), graupel/rime (R), plate (P1), needle/column (N/C), and small particles/germ (G).

Description

The dataset includes a version of original images and a version of images with augmentation (rotation, exposure, blur, salt & pepper noise). Each version consists of a training set and a validation set, each with the same structure: images and labels. The images are in JPEG format, each with at least 16 snow particles. The labels are in TXT format, including the classes (in numbers: 0-aggregate/irregular/I, 1-dendrite/P2, 2-graupel/rime/R, 3-plate/P1, 4-needle/column/N/C, and 5-small particles/germ/G) assigned to the snow particles, and the bounding boxes (including location coordinates and normalized height/width of the box) for detecting them.

Referenced by

Li, J.; Guala, M.; Hong, J. Snow Particle Analyzer for Simultaneous Measurements of Snow Density and Morphology. Journal of geophysical research. Atmospheres 2023.
https://doi.org/10.1029/2023JD038987

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License

CC0 1.0 Universal
 http://creativecommons.org/publicdomain/zero/1.0/

Publisher

Collections

Data Repository for U of M (DRUM)

Funding information

The work related to this dataset is supported by the National Science Foundation (Program Manager, Nicholas Anderson) under grant NSF-AGS-1822192.

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Previously Published Citation

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Suggested citation

Li, Jiaqi; Guala, Michele; Hong, Jiarong. (2022). Database of snow holograms collected from 2019 to 2022 for machine learning training or other purposes. Retrieved from the Data Repository for the University of Minnesota (DRUM), https://doi.org/10.13020/7akr-8n50.

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Readme_Li_2022.txt
Description of data
(4.73 KB)

Snow_particles_original_yolov5pytorch.zip
Training and Validation Data with original images
(166.35 MB)

Snow particles_augmented_yolov5pytorch.zip
Training and Validation Data with augmented images
(487.43 MB)

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