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README.md

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-license: mit
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+license: mit
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+# Grape Leaf Disease Detection with KANConv2D
+
+## Description
+This project used a custom convolutional layer, KANConv2D, to build a convolutional neural network (CNN) for detecting Esca disease in grape leaves. The KANConv2D layer incorporates kernel adaptive networks to enhance the performance of traditional Conv2D layers.
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+For more on KAN see: https://arxiv.org/pdf/2406.13155
+
+Final accuracy was 84.17%
+
+## Features
+- Custom KANConv2D layer with kernel adaptive networks
+- Data augmentation for robust training
+- Early stopping to prevent overfitting
+- Class weighting to handle imbalanced datasets
+
+## Model Architecture
+The CNN model consists of:
+- Multiple KANConv2D layers
+- Max pooling layers
+- Dense and dropout layers
+- Binary output with sigmoid activation
+
+## Training
+The model is compiled with the Adam optimizer, binary cross-entropy loss, and accuracy as the metric. Class weights are adjusted to reduce false negatives for Esca detection. The model is trained with early stopping to restore the best weights based on validation loss.