Uploading None in /Users/camille.brianceau/aramis/maps

README.md

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+# Model Card for None  
+This model was trained with ClinicaDL. You can find here the   
+## General information  
+## Architecture  
+This model was trained for **classification** and the architecture chosen is **Conv4_FC3**.  
+**dropout**: 0.0  
+**latent_space_size**: 2  
+**feature_size**: 1024  
+**n_conv**: 4  
+**io_layer_channels**: 8  
+**recons_weight**: 1  
+**kl_weight**: 1  
+**normalization**: batch  
+**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