Uploading maps-test in /Users/camille.brianceau/aramis/DATA/maps

README.md

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+---
+language: en
+library_name: clinicadl
+tags:
+- clinicadl
+license: mit
+---
+# Model Card for maps-test  
+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  

maps.json

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+{
+    "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\u00d7208\u00d7179, 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
+    ]
+}

split-0/best-loss/model.pth.tar

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split-1/best-loss/model.pth.tar

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split-2/best-loss/model.pth.tar

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split-3/best-loss/model.pth.tar

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split-4/best-loss/model.pth.tar

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