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Pair Extraordinaire Achievement Test #10

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Pair Extraordinaire Achievement Test #10

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73a9da5
Add Testing Pair Extraordinaire achievement
VisionStack-404 Jun 6, 2026
ccdfe1a
Merge branch 'main' into main
VisionStack-404 Jun 6, 2026
863677f
Clean up and fixied the bugs
VisionStack-404 Jun 15, 2026
9ba079c
added the try and exception block
VisionStack-404 Jun 15, 2026
f1a7a32
optimized the model and added the layers
VisionStack-404 Jun 15, 2026
915db93
updated the calculated the function
VisionStack-404 Jun 15, 2026
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1 change: 1 addition & 0 deletions README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -181,6 +181,7 @@ The PyTorch code resides in `backend.py` and outlines the custom `DementiaMultim
## 馃摐 Academic Reference & License

This project is licensed under the **Apache License 2.0**. Feel free to use, modify, and distribute this codebase for academic and clinical research.
Testing Pair Extraordinaire Achievement 馃殌
Working towards Pull Shark achievement 馃
working with pull requests
Testing GitHub Pull Request Achievement
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9 changes: 4 additions & 5 deletions backend.py
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Expand Up @@ -9,7 +9,7 @@

# ==========================================
# DATASETS: Where the data is loaded and used
# ==========================================

class DementiaMultimodalDataset(Dataset):
"""
Custom PyTorch Dataset that loads both MRI (ADNI/OASIS)
Expand All @@ -28,9 +28,9 @@ def __len__(self):
def __getitem__(self, idx):
row = self.metadata.iloc[idx]

# 1. Load the dataset for Branch 1 (OASIS / ADNI MRI)

mri_path = os.path.join(self.mri_dir, row['mri_filename'])
# Load 2D/3D MRI slide (converting to RGB for ResNet compatibility here)

mri_image = Image.open(mri_path).convert('RGB')

if self.transform:
Expand Down Expand Up @@ -78,7 +78,7 @@ def __init__(self, tabular_input_dim=128, lstm_hidden_dim=256, num_classes=3):
nn.Softmax(dim=1)
)

# --- Classification Layer ---

self.classifier = nn.Sequential(
nn.Linear(fusion_dim, 256),
nn.ReLU(),
Expand Down Expand Up @@ -151,7 +151,6 @@ def forward(self, mri_imgs, speech_seq):
# Assuming MFCC features have 40 coefficients per frame
model = MultimodalDementiaModel(tabular_input_dim=40, lstm_hidden_dim=256, num_classes=3)

# --- 5. Evaluation / Accuracy Calculation Loop (Example) ---
def calculate_accuracy(model, data_loader, device='cpu'):
model.eval()
correct = 0
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