# 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