region:us
# Model Card for None  
architecture: Conv4_FC3  
multi_network: False  
dropout: 0.0  
latent_space_dimension: 64  
latent_space_size: 2  
selection_metrics: ['loss']  
label: diagnosis  
selection_threshold: 0.0  
gpu: True  
n_proc: 32  
batch_size: 32  
evaluation_steps: 20  
seed: 0  
deterministic: False  
compensation: memory  
transfer_path: ../../autoencoders/exp3/maps  
transfer_selection_metric: loss  
use_extracted_features: False  
multi_cohort: False  
diagnoses: ['AD', 'CN']  
baseline: True  
normalize: True  
data_augmentation: False  
sampler: random  
n_splits: 5  
epochs: 200  
learning_rate: 1e-05  
weight_decay: 0.0001  
patience: 10  
tolerance: 0.0  
accumulation_steps: 1  
optimizer: Adam  
preprocessing_dict: {'preprocessing': 't1-linear', 'mode': 'roi', 'use_uncropped_image': False, 'roi_list': ['leftHippocampusBox', 'rightHippocampusBox'], 'uncropped_roi': False, 'prepare_dl': False, 'file_type': {'pattern': '*space-MNI152NLin2009cSym_desc-Crop_res-1x1x1_T1w.nii.gz', 'description': 'T1W Image registered using t1-linear and cropped (matrix size 169×208×179, 1 mm isotropic voxels)', 'needed_pipeline': 't1-linear'}}  
mode: roi  
network_task: classification  
caps_directory: $WORK/../commun/datasets/adni/caps/caps_v2021  
tsv_path: $WORK/Aramis_tools/ClinicaDL_tools/experiments_ADDL/data/ADNI/train  
validation: KFoldSplit  
num_networks: 2  
label_code: {'AD': 0, 'CN': 1}  
output_size: 2  
input_size: [1, 50, 50, 50]  
loss: None  
