DeepParticleNet
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maxfrei750
A deep neural network for particle size and shape analysis.
:exclamation: ATTENTION :exclamation:
This code is no longer maintained and has been superseeded by the paddle toolbox.
DeepParticleNet
This repository is a toolbox for the easy, deep learning-based primary particle size analysis of agglomerated, aggregated, partially sintered or simply occluded particles. It accompanies the following publication:
The utilized convolutional neural network was inspired by the Mask R-CNN architecture, developed by He et al. and is based on an implementation of Abdulla, realized with Keras and TensorFlow, controlled by Python.
Table of Contents
- DeepParticleNet
- Table of Contents
- Examples
- Citation
- Setup
- Getting started
Examples
Detection
PSD Measurement
Citation
If you use this repository for a publication, then please cite it using the following bibtex-entry:
@article{Frei.2019,
author = {Frei, Max and Kruis, Frank Einar},
year = {2019},
title = {Image-Based Size Analysis of Agglomerated and Partially Sintered Particles via Convolutional Neural Networks},
url = {https://doi.org/10.1016/j.powtec.2019.10.020}
}
Setup
CPU only (Linux & Windows)
Click to expand ...
- Install docker.
- Open a command line.
- Clone this repository:
git clone --recurse-submodules https://github.com/maxfrei750/DeepParticleNet.git - Change into the folder of the repository:
cd DeepParticleNet - Spin up the docker container (adjust paths according to your folder structure):
docker run -i --name deepparticlenet -p 8888:8888 -p 6006:6006 -v /path/to/code:/tf -v /path/to/datasets:/tf/datasets -v /path/to/logs:/tf/logs maxfrei750/deepparticlenet:cpu
Optional: Start Tensorboard
- Open a command line.
- Start Tensorboard:
docker exec -i deepparticlenet tensorboard --logdir=/tf/logs - Access
localhost:6006in your browser.
GPU support (Linux)
Click to expand ...
- Install docker.
- Install nvidia-docker.
- Open a command line.
- Clone this repository:
git clone --recurse-submodules https://github.com/maxfrei750/DeepParticleNet.git - Change into the folder of the repository:
cd DeepParticleNet - Spin up the docker container (adjust paths according to your folder structure):
nvidia-docker run -i --shm-size=1g --ulimit memlock=-1 --name deepparticlenet -p 8888:8888 -p 6006:6006 -v /path/to/code:/tf -v /path/to/datasets:/tf/datasets -v /path/to/logs:/tf/logs maxfrei750/deepparticlenet:gpu
Optional: Start Tensorboard
- Open a command line.
- Start Tensorboard:
docker exec -i deepparticlenet tensorboard --logdir=/tf/logs - Access
localhost:6006in your browser.
GPU support (Windows)
Click to expand ...
Nvidia-docker does not support Windows. Therefore, if you are running Windows and need GPU support, then you need to setup a python environment (e.g. conda).
- Install conda.
- Open a command line.
- Clone this repository:
git clone --recurse-submodules https://github.com/maxfrei750/DeepParticleNet.git - Change into the folder of the repository:
cd DeepParticleNet - Create a new conda environment:
conda env create --file dpn-gpu-environment.yml - Activate the new conda environment:
activate dpn-gpu-env - Start jupyter lab:
jupyter lab
Optional: Start Tensorboard
- Open a command line.
- Activate the conda environment:
activate dpn-gpu-env - Start Tensorboard:
tensorboard --logdir=/path/to/logs - Access
localhost:6006in your browser.
Getting started
- Copy the jupyter token from your command line.
- Enter the jupyter server by accessing
localhost:8888/labin your browser and pasting the jupyter token that you just copied.
Metadata
Owner
maxfrei750
Metadata
A deep neural network for particle size and shape analysis.