This repo houses the initial scripts for building a deep learning app on BioData Catalyst powered by Seven Bridges. These scripts will be used to test training scalability, among other issues.
train.py creates a VGG-16 model for single-channel image classification.
| Arg | Description | Type | Values | Required |
|---|---|---|---|---|
| --data_csv | Path to CSV file pointing to images/labels | string | YES | |
| --image_column | Column name for images | string | YES | |
| --label_column | Column name for labels | string | YES | |
| --test_ratio | Percentage for testing data | float | 0.3 (Default) | |
| --epochs | Number of training epochs | int | 15 (Default) | |
| --batch_size | Training batch size | int | 8 (Default) | |
| --output | Specify file name for output | string | 'model' (Default) | |
| --auto_resize | Auto-resize to min height/width of image set | store_true |
get_sizes.py --data_csv /path/to/file.csv --image_column image_path_column_name will create a CSV containing the image name, SimpleITK image shape, and Numpy array shape. It will also print this information to the console.
Content type
Image
Digest
Size
2.9 GB
Last updated
about 5 years ago
docker pull tmajarian/helxplatform_gil