| # Weights & Biases | |
| USE_WANDB: True #@param {type:"boolean"} | |
| WANDB_API_KEY: 'REPLACE_ME' #@param {type:"string"} | |
| # Data | |
| DATASET_PATH: 'REPLACE_ME' #@param {type:"string"} | |
| MIN_VERTEBRAE_LEVEL: 8 #@param {type:"number"} | |
| INPUT_DIM: 3 #@param [2, 3] {allow-input: true} | |
| INPUT_SIZE: 64 #@param {type:"slider", min:32, max:112, step:4} | |
| OVERSAMPLING: True | |
| FOLD: 1 | |
| # Mask | |
| # one of 'none', 'channel' (mask is additional input channel), 'apply' (single vertebra mask is applied to input), | |
| # 'apply_all' (visible vertebra mask applied to input), 'crop' (mask is used to crop input) | |
| MASK: 'none' | |
| # Whether coordinates are provided in additional channels | |
| # See Liu, Rosanne, et al. "An intriguing failing of convolutional neural networks and the CoordConv solution" (https://arxiv.org/pdf/1807.03247.pdf) | |
| COORDINATES: False | |
| # Whether to save the best-performing model (wrt validation F1) | |
| SAVE_MODEL: False | |
| # Data Augmentation | |
| TRANSFORMS: ['modelsgenesis', 'intensity', 'spatial3d-simple'] | |
| # Task | |
| TASK: "detection" # "detection", "grading", "simple_grading" | |
| LOSS: 'binary_cross_entropy' # "ordinal_regression", "cross_entropy", "focal" | |
| # Model | |
| BATCH_SIZE: 16 #@param {type:"slider", min:32, max:512, step:32} | |
| LEARNING_RATE: 0.001 #@param {type:"number"} | |
| # AUTO_LR_FIND = True #@param {type: 'boolean'} | |
| DROPOUT: 0.3 #@param {type:"number"} | |
| WEIGHTED_LOSS: True #@param {type:"boolean"} | |
| EARLY_STOPPING_PATIENCE: 40 #@param {type:"number"} | |
| # Available backbones: | |
| # - DenseNet121 | |
| # - ModelsGenesis (3D) | |
| # - UNet3D | |
| MODEL_NAME: 'UNet3D' #@param ["DenseNet121"] {allow-input: true} | |
| # Identify all modules to freeze by name, e.g. ['down_tr64', 'down_tr128', 'down_tr256', 'down_tr512'] for Models Genesis/UNet3D | |
| FROZEN_LAYERS: [] |