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.gitattributes

@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+Pred_2d.png filter=lfs diff=lfs merge=lfs -text
+Prediction_3D.png filter=lfs diff=lfs merge=lfs -text

config.txt

@@ -0,0 +1,41 @@
+BraTS 3D Segmentation Training Configuration with MONAI + Accelerate
+==================================================
+
+Training Date: 2025-10-09 06:01:59
+
+Data Configuration:
+  Data Directory: Data/BraTS2020_TrainingData/MICCAI_BraTS2020_TrainingData
+  ROI Size (Inference): (128, 128, 128)
+  Number of Classes: 3
+  Train Split: 80.0%
+  Val Split: 5.0%
+  Test Split: 15.0%
+  No Augmentation (Original Images Only)
+
+Model Configuration:
+  Model: SegResNet
+  Input Channels: 4
+  Output Channels: 3
+  Initial Filters: 16
+
+Training Configuration:
+  Framework: Accelerate
+  Mixed Precision: fp16
+  Optimizer: Adam
+  Batch Size (per GPU): 2
+  Batch Size (Val/Test per GPU): 1
+  Epochs: 150
+  Learning Rate: 0.0001
+  Weight Decay: 1e-05
+  Validation Interval: 5
+  Use Caching: False
+
+Hardware Configuration:
+  Number of Workers per GPU: 8
+  Pin Memory: True
+  Prefetch Factor: 4
+
+Class Names (Multi-Label):
+  Channel 0: Tumor Core (TC)
+  Channel 1: Whole Tumor (WT)
+  Channel 2: Enhancing Tumor (ET)

model_final_20251009_122935.onnx

@@ -0,0 +1,3 @@
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+oid sha256:3c961ac9068308a6a0a86d31a5021f282d97f6581ffd9163a4fce2284f2186c2
+size 18874395

summary.json

@@ -0,0 +1,76 @@
+{
+    "total_epochs": 150,
+    "total_time_seconds": 23254.743891716003,
+    "total_time_hours": 6.459651081032223,
+    "batch_size_per_gpu": 2,
+    "num_processes": 2,
+    "mixed_precision": "fp16",
+    "final_train_loss": 0.11351284384727478,
+    "final_train_metrics": {
+        "precision": [
+            0.9284262657165527,
+            0.9326722621917725,
+            0.8191995024681091
+        ],
+        "recall": [
+            0.9270820021629333,
+            0.9292428493499756,
+            0.8744466304779053
+        ],
+        "accuracy": [
+            0.9996001720428467,
+            0.9991011619567871,
+            0.9927959442138672
+        ],
+        "dice": [
+            0.9271973371505737,
+            0.9303600788116455,
+            0.8458120822906494
+        ]
+    },
+    "final_val_metrics": {
+        "precision": [
+            0.8964494466781616,
+            0.9273992776870728,
+            0.7346808910369873
+        ],
+        "recall": [
+            0.8001397848129272,
+            0.8460437655448914,
+            0.7222132682800293
+        ],
+        "accuracy": [
+            0.9991061687469482,
+            0.9987545013427734,
+            0.8885079026222229
+        ],
+        "dice": [
+            0.8246815204620361,
+            0.8800930976867676,
+            0.7170625329017639
+        ]
+    },
+    "final_test_loss": 0.1920088231563568,
+    "final_test_metrics": {
+        "precision": [
+            0.8555397391319275,
+            0.93755042552948,
+            0.7489745616912842
+        ],
+        "recall": [
+            0.7621530294418335,
+            0.838932991027832,
+            0.7132463455200195
+        ],
+        "accuracy": [
+            0.9988489151000977,
+            0.9985865354537964,
+            0.9637566804885864
+        ],
+        "dice": [
+            0.7748545408248901,
+            0.8739949464797974,
+            0.7190028429031372
+        ]
+    }
+}

test_metrics.csv

@@ -0,0 +1,2 @@
+epoch,loss,avg_dice,avg_precision,avg_recall,avg_accuracy,Tumor Core (TC)_dice,Tumor Core (TC)_precision,Tumor Core (TC)_recall,Tumor Core (TC)_accuracy,Whole Tumor (WT)_dice,Whole Tumor (WT)_precision,Whole Tumor (WT)_recall,Whole Tumor (WT)_accuracy,Enhancing Tumor (ET)_dice,Enhancing Tumor (ET)_precision,Enhancing Tumor (ET)_recall,Enhancing Tumor (ET)_accuracy,epoch_time_sec
+150,0.192009,0.789284,0.847355,0.771444,0.987064,0.774855,0.855540,0.762153,0.998849,0.873995,0.937550,0.838933,0.998587,0.719003,0.748975,0.713246,0.963757,0.00