The dataset contains images categorized into sehat and tidak sehat , organized into train , test , and validation folders, each with subfolders for each class ( /sehat and /tidak sehat ). Images are in JPEG or PNG format with a recommended resolution of 240x240 pixels, suitable for the VGG16 model’s input requirements. The dataset is intended for deep learning applications, viewable with standard image viewers, and executable with Python, particularly using TensorFlow and Keras . To access and run the VGG16 model, Google Colab or Jupyter Notebook can be used for cloud. For processing, an image data generator is set up to normalize the images, while VGG16 (with pre-trained ImageNet weights) serves as the base model with added dense layers for binary classification between sehat and tidak sehat . The model can then be compiled with an optimizer (e.g., Adam) and trained on the data with appropriate evaluation on validation and test sets.

This repository contains a Python script for classifying apple leaf diseases using a Vision Transformer (ViT) model. The dataset used is the Plant Village dataset, which contains images of apple leaves with four classes: Healthy, Apple Scab, Black Rot, and Cedar Apple Rust. The script includes data preprocessing, model training, and evaluation steps.

• Introduction
• Code Explanation
• Steps for Implementation
• Example Usage
• Conclusion

The goal of this project is to classify apple leaf diseases using a Vision Transformer (ViT) model. The dataset is divided into four classes: Healthy, Apple Scab, Black Rot, and Cedar Apple Rust. The script includes data preprocessing, model training, and evaluation steps.

• The script starts by importing necessary libraries such as matplotlib, seaborn, numpy, pandas, tensorflow, and sklearn. These libraries are used for data visualization, data manipulation, and building/training the deep learning model.

• The walk_through_dir function is used to explore the dataset directory structure and count the number of images in each class.
• The dataset is divided into Train, Val, and Test directories, each containing subdirectories for the four classes.

• The script uses ImageDataGenerator from Keras to apply data augmentation techniques such as rotation, horizontal flipping, and rescaling to the training data. This helps in improving the model's generalization ability.
• Separate generators are created for training, validation, and test datasets.

• The script defines a Patches layer that extracts patches from the images. This is a crucial step in Vision Transformers, where images are divided into smaller patches that are then processed by the transformer.
• The script visualizes these patches for different patch sizes (32x32, 16x16, 8x8) to understand how the image is divided.

• The script defines a Vision Transformer (ViT) model using TensorFlow and Keras. The model is compiled with the Adam optimizer and categorical cross-entropy loss.
• The model is trained for a specified number of epochs, and the training history is stored for later analysis.

• After training, the model is evaluated on the test dataset. The script generates a confusion matrix and a classification report to assess the model's performance.
• The confusion matrix is visualized using seaborn to provide a clear understanding of the model's predictions.

• The script includes functionality to visualize misclassified images, which helps in understanding where the model is making errors.

• The script demonstrates how to fine-tune the model by adjusting the learning rate and re-training the model.

1. Dataset Preparation

   • Ensure that the dataset is organized into Train, Val, and Test directories, with each directory containing subdirectories for each class (Healthy, Apple Scab, Black Rot, Cedar Apple Rust).

2. Install Required Libraries

   • Install the necessary Python libraries using pip:

     pip install tensorflow matplotlib seaborn numpy pandas scikit-learn

3. Run the Script

   • Execute the script in a Python environment. The script will automatically:
     • Load and preprocess the dataset.
     • Apply data augmentation.
     • Train the Vision Transformer model.
     • Evaluate the model and generate performance metrics.

4. Analyze Results

   • Review the confusion matrix and classification report to understand the model's performance.
   • Visualize misclassified images to identify potential areas for improvement.

5. Fine-Tuning

   • Experiment with different patch sizes, learning rates, and data augmentation techniques to improve the model's accuracy.

A spatio-temporal (ST) machine learning (ML) model for security-constrained unit commitment (SCUC) solution acceleration. The ML architecture with GNN and LSTM layers. Includes two models, one for node prediction to predict generator commitment status, and another for edge prediction, which predicts congested lines in the system. The predictions from the two models are then used to reduce the number of variables and constraints in a SCUC problem.NOTE: Codes are implemented in Python. ML model uses Keras, Tensorflow and Spektral (GNN) libraries. Optimization is implemented using Pyomo in python. A solver license (cplex/gurobi) is required for pyomo to run.