Test data and models for the FlowCam data processing pipeline

Testing data for the FlowCam data processing pipeline The data provided are fully processed and can be used to test each pipeline component. You can download the scripts at https://github.com/TimWalles/LabelChecker_Pipeline The LabelChecker software can be downloaded from https://github.com/TimWalles/LabelChecker/releases/latest Data description There are two datasets available for testing, a freshwater dataset and a marine dataset. Each of the ZIP files contains a data folder and a model folder. The data folder has 3 subfolders corresponding to the data export of a FlowCam run. You have the TIF collage files, a CSV file with the sample name containing all the parameters measured by the FlowCam, and a LabelChecker_<sample> CSV file generated by the preprocessing.py script. To use the model, place the folder as-is in the respective model folder under 'services': |--src |--services |-- ProcessData.py |-- config.py |-- classification |-- ObjectClassification |-- models |-- <PASTE HERE> |-- ... |-- ... |--...|-- ... Pipeline scripts You can run the preprocessing.py script directly on the files by including the -R (reprocess) argument. The PreprocessingTrue and Label... columns will remain the same. Otherwise, you can do it by removing the LabelChecker CSV from the folders and start from scratch. When running the classification.py script, you can get new predictions on the data. In this case, only the LabelPredicted column will be updated and the validated labels (LabelTrue column) will not be lost. You could also use these files to try out the train_model.ipynb, although the resulting model will not be very good with so little data. We recommend trying it with your own data. LabelChecker These files can be used to test LabelChecker. You can open them one by one or all at once and try all functionalities. We provide the labels for each dataset, but you can also try to make your own.