This project aims to perform well at instance segmentation on the BBBC006 cells dataset. We tested UNet over several configurations including the loss function, evaluation function and the datasets.
These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.
For running the training you will use the main.py file. In case you're using a sort of cluster, you should see pbs_unet.pbs. In either case, the options for configuration are the same.
Options:
-h, --help Show this help message and exit
-e EPOCHS, --epochs=EPOCHS
Number of epochs
-b BATCHSIZE, --batch-size=BATCHSIZE
Batch size
-l LR, --learning-rate=LR
Learning rate
-a LOAD, --load=LOAD Load model file
-r RUNS, --runs=RUNS How many runs
-d DATASET, --dataset=DATASET
Which dataset should use.
-g GT, --groundtruth=GT
Which gt should use.
-s SAVEDIR, --savedir=SAVEDIR
Which folder should use for checkpoints.
-t VAL_PERC, --val-percentage=VAL_PERC
Validation Percentage
-i N_CHANNELS, --n-channels=N_CHANNELS
Number of channels of the inputs.
-c N_CLASSES, --n-classes=N_CLASSES
Number of classes of the output.
-o OPTIMIZER, --optimizer=OPTIMIZER
Optimizer to use.
-f LOSS, --loss=LOSS Loss functios to use.
-v EVALUATION, --evaluation=EVALUATION
Evaluation function to use.
If you want to use another optimizer or loss/evaluation function you must code it on main.py
For running the visualizations you will use the visualization folder.
If you want to see how a trained model performs out, you should use the result_visualization.py file. These report will create an image of the performance (Loss, Accuracy), an image of the outputs of the model and an image of the gt to compare.
Options:
-h, --help Show this help message and exit
-f FOLDER, --folder=FOLDER
Folder containing the csv files.
-t TITLE, --title=TITLE
Title of the plot
-i N_CHANNELS, --n-channels=N_CHANNELS
Number of channels of the inputs.
-c N_CLASSES, --n-classes=N_CLASSES
Number of classes of the output.
-l LOAD, --load=LOAD Path and name of the load file.
-d DATASET, --dataset=DATASET
Dataset for the inputs.
-g GT, --gt=GT Gt to compare.
If you want to show how the model performs out compared with another one, you should use the compare_two_results.py file. These report will create an image of the performance (Loss, Accuracy) of both models.
Options:
-h, --help Show this help message and exit
-f FOLDER, --folder=FOLDER
Folder containing the first to compare csv files.
-c F_COMPARE, --folder_compare=F_COMPARE
Folder containing the second to compare csv files.
-t TITLE, --title=TITLE
Title of the plot
-s SAVEDIR, --savedir=SAVEDIR
Which folder should use for saving the results.
- Mauro Méndez - mamemo
See also the list of contributors who participated in this project.
This project is licensed under the MIT License - see the LICENSE.md file for details
- All of the UNet implementations that cross into my way.
- Jose Carranza - maeotaku - for helping in the misc files.
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