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