Particle Dataset

Welcome to the GitHub page of DeepTrackAI's Particle dataset. The Particle dataset is a collection of movies of optically-trapped particles used for training and evaluating deep learning models.

Description

The Particle dataset contains 2 movies in avi format. Each frame is an RGB picture of a trapped spherical particle.

• Dataset Size: 2 movies
• Number of Frames: 100 frames per movie
• Frame Size: 120x120 pixels
• Color: RGB

Usage

To use the Particle dataset in your project:

1. Clone this repository to your local machine.
2. Import the dataset into your machine learning framework of choice.
3. Train or evaluate your models using the dataset.

Download via Command Line

To clone the repository and access the Particle dataset:

git clone https://github.com/DeepTrackAI/particle_dataset
cd particle_dataset

Download Programmatically in Python

If you want to load the dataset directly into a Python script or Jupyter notebook:

import requests
from io import BytesIO
from zipfile import ZipFile

# URL to the repository (modify this if the dataset is hosted in a specific location or file)
DATASET_URL = 'https://github.com/DeepTrackAI/particle_dataset/raw/main/mnist.zip'

response = requests.get(DATASET_URL)
with ZipFile(BytesIO(response.content)) as z:
    z.extractall()

# Now you can load the dataset using your preferred library, e.g., deeplay, PyTorch, TensorFlow.

Acknowledgements

The Particle dataset was originally created by Saga Helgadottir, Aykut Argun & Giovanni Volpe.

If you use this dataset, please cite:

https://doi.org/10.1364/OPTICA.6.000506:

Saga Helgadottir, Aykut Argun, and Giovanni Volpe.
"Digital video microscopy enhanced by deep learning."
Optica 6.4 (2019): 506-513.

Benjamin Midtvedt, Saga Helgadottir, Aykut Argun, Jesús Pineda, Daniel Midtvedt, Giovanni Volpe.
"Quantitative Digital Microscopy with Deep Learning."
Applied Physics Reviews 8 (2021), 011310.
https://doi.org/10.1063/5.0034891

License

The Particle dataset is made available under the terms of the Creative Commons Attribution-Share Alike 3.0 license.

Contributing

If you find any issues with the dataset or have suggestions for improvements, please open an issue or submit a pull request.