Update README.md

README.md

@@ -1,36 +1,70 @@
 ---
 license: apache-2.0
-datasets:
-  - JLB-JLB/seizure_eeg_dev
-metrics:
-  - accuracy: 0.86
+library_name: pytorch
 tags:
-  - biology
+  - seizure-detection
+  - medical-imaging
+  - cnn
   - healthcare
   - eeg
-  - seizure-detection
+pipeline_tag: image-classification
 ---
 
-# Seizure Detection EEG Model
+# SeizureDetectionCNN
 
 ## Model Description
 
-This model is designed to detect seizures from EEG (Electroencephalogram) data.
+SeizureDetectionCNN is a convolutional neural network designed for binary classification of seizure events using EEG data converted to images. The model employs a simple yet effective architecture with two convolutional layers followed by batch normalization and three fully connected layers.
+
+### Model Architecture
+
+```python
+SeizureDetectionCNN(
+  (conv1): Conv2d(1, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+  (pool): MaxPool2d(kernel_size=2, stride=2, padding=0)
+  (conv2): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+  (bn1): BatchNorm2d(32)
+  (bn2): BatchNorm2d(64)
+  (dropout): Dropout(p=0.5)
+  (fc1): Linear(in_features=4096, out_features=120)
+  (fc2): Linear(in_features=120, out_features=32)
+  (fc3): Linear(in_features=32, out_features=2)
+)
+```
+
+### Input Description
+
+Input images are preprocessed to 32x32 grayscale
+Images are normalized with mean=[0.5] and std=[0.5]
+Input tensor shape: (batch_size, 1, 32, 32)
 
-### Performance
+### Preprocessing
+pythonCopytransforms.Compose([
+    transforms.Grayscale(),
+    transforms.Resize((32, 32)),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.5], std=[0.5])
+])
 
-- Accuracy: 86%
+## Training Procedure
 
-## Intended Use
+### Architectural Features
 
-This model is intended for research purposes in analyzing EEG data for seizure detection patterns.
+2 Convolutional layers with ReLU activation
+Batch Normalization after each convolutional layer
+MaxPooling with kernel size 2
+Dropout (p=0.5) for regularization
+3 Fully connected layers
 
-## Training Data
+### Parameters
 
-The model was trained on the `JLB-JLB/seizure_eeg_dev` dataset, which contains EEG recordings.
+Total Parameters: ~500K
+Input Channels: 1 (grayscale)
+Output Classes: 2 (binary classification)
 
-## Limitations
+## Intended Uses & Limitations
+### Intended Uses
 
-- This is a development model and should not be used for clinical decisions
-- Performance may vary across different EEG recording setups
-- Should be used only as part of a comprehensive medical evaluation
+Research and development in seizure detection
+Processing of EEG data converted to image format
+Binary classification of seizure/non-seizure events