maxfrei750/ParticleMixtureAnalysis

ParticleMixtureAnalysis

This repository accompanies the following publication:

Frei, Kruis — Image-Based Analysis of Dense Particle Mixtures via Mask R-CNN

dark particles	light particles

Installation

1. Install conda for your operating system.

2. Open a command line.

3. Clone this repository: git clone --recurse-submodules https://github.com/maxfrei750/ParticleMixtureAnalysis.git

4. Change into the folder of the repository.

5. Create a new conda environment: conda env create --file external/paddle/environment.yaml

Training

1. Download the datasets.zip file and extract it at the project root.

2. Activate the conda environment: conda activate paddle

3. Run the training: python train_model.py --config-dir=configs/MaskRCNN --config-name=MultiClassSynthetic400SyntheticValidation400Detections

4. The results of the inference can be found in the logs folder.

Inference

1. Download the datasets.zip file and extract it at the project root.

2. Either complete the training (see above) or download the model.zip file and extract it at the project root.

3. Activate the conda environment: conda activate paddle

4. Run the model on a dataset: python test_model_on_dataset.py MultiClassSynthetic400SyntheticValidation400Detections validation_real_merged_by_vote

5. The results of the inference can be found in the output folder.

Citation

If you use this repository for a publication, then please cite it using the following bibtex-entry:

@article{Frei.2022,
    author = {Frei, Max and Kruis, Frank Einar},
    year = {2022},
    title = {Image-Based Analysis of Dense Particle Mixtures via Mask R-CNN},
    url = {https://doi.org/10.3390/eng3010007}
}