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--- |
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language: en |
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library_name: clinicadl |
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tags: |
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- clinicadl |
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license: mit |
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--- |
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# Model Card for None |
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This model was trained with ClinicaDL. You can find here the |
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## General information |
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## Architecture |
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This model was trained for **classification** and the architecture chosen is **Conv4_FC3**. |
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**dropout**: 0.0 |
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**latent_space_size**: 2 |
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**feature_size**: 1024 |
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**n_conv**: 4 |
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**io_layer_channels**: 8 |
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**recons_weight**: 1 |
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**kl_weight**: 1 |
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**normalization**: batch |
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**architecture**: Conv4_FC3 |
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**multi_network**: False |
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**dropout**: 0.0 |
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**latent_space_dimension**: 64 |
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**latent_space_size**: 2 |
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**selection_metrics**: ['loss'] |
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**label**: diagnosis |
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**selection_threshold**: 0.0 |
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**gpu**: True |
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**n_proc**: 32 |
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**batch_size**: 32 |
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**evaluation_steps**: 20 |
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**seed**: 0 |
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**deterministic**: False |
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**compensation**: memory |
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**transfer_path**: ../../autoencoders/exp3/maps |
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**transfer_selection_metric**: loss |
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**use_extracted_features**: False |
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**multi_cohort**: False |
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**diagnoses**: ['AD', 'CN'] |
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**baseline**: True |
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**normalize**: True |
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**data_augmentation**: False |
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**sampler**: random |
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**n_splits**: 5 |
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**epochs**: 200 |
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**learning_rate**: 1e-05 |
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**weight_decay**: 0.0001 |
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**patience**: 10 |
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**tolerance**: 0.0 |
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**accumulation_steps**: 1 |
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**optimizer**: Adam |
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**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'}} |
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**mode**: roi |
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**network_task**: classification |
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**caps_directory**: $WORK/../commun/datasets/adni/caps/caps_v2021 |
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**tsv_path**: $WORK/Aramis_tools/ClinicaDL_tools/experiments_ADDL/data/ADNI/train |
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**validation**: KFoldSplit |
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**num_networks**: 2 |
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**label_code**: {'AD': 0, 'CN': 1} |
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**output_size**: 2 |
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**input_size**: [1, 50, 50, 50] |
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**loss**: None |
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