Python scripts for training CNNs for particle classification
Project description
Particle Classification
Python scripts for particle classification
Used by particle trieur to perform model training.
Installation
conda create -n miso python=3.9
conda activate miso
conda install -c conda-forge cudatoolkit=11.2 cudnn=8.1.0
pip install tensorflow==2.10.1
pip install miso
Updating
conda activate miso
pip install -U miso
Command line interface (CLI)
Train a model
TBD
Classify a folder of images
Classifies a folder of images and saves the result in a CSV. This CSV can be imported into Particle Trieur. If the images are organised by sample into subfolders, will extract the sample as the subfolder name, else specify the sample name manually.
Usage: python -m miso classify-folder [OPTIONS]
Classify images in a folder and output the results to a CSV file.
Options:
-m, --model PATH Path to the model information. [required]
-i, --input PATH Path to the directory containing images.
[required]
-o, --output PATH Path where the output CSV will be saved.
[required]
-b, --batch_size INTEGER Batch size for processing images. [default:
32]
-f, --in_samples / --no-in_samples
Set this flag if images are stored in
subfolders, using the subfolder names as
sample labels. [default: no-in_samples]
-s, --sample TEXT Default sample name if not using
subdirectories. [default: unknown]
--unsure_threshold FLOAT Threshold below which predictions are
considered unsure. [default: 0.0]
Project details
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