Update README.md

README.md

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 license: apache-2.0
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-Real vs AI-Generated Image Classification
+Real art vs AI-Generated art image classification
 
-This project provides a Convolutional Neural Network (CNN) model for classifying images as either 'real' or 'fake'.
+This project provides a Convolutional Neural Network (CNN) model for classifying images as either 'real art' or 'fake art'.
 CNN is a type of deep learning model specifically designed to process and analyze visual data by applying convolutional layers that automatically detect patterns and features in images.
-Our CNN model is based on 2,800 real images and AI-generated images, which are divided equally.
-
 Our goal is to accurately classify the source of the image with at least 85% accuracy and achieve at least 80% in the Recall test. 
 
-5. Installation Instructions
-6. Usage Instructions
-7. Model Architecture
-8. Training Details
-9. Evaluation
-10. Examples
-11. Contributing
-12. License
+Installation instructions
+The following libraries or packages are required: numpy, pandas, tensorflow, keras, matplotlib, sklearn, cv2.  
+We prepare the data for the model by sorted the images into 2 types of folders which are divided equally(real art- labeled as 0, fake art- labeled as 1).
+Our CNN model is based on 2,800 images that have been resized and normalized, the files formats is PNG‬, JPG‬. 
+The images are divided into a training set that contains 90% from data and a testing set that contains the remaining 10%.
+
+CNN model architecture
+Convolutional Layers: for feature extraction from images, applying 32 or 64 filters with a size of 3x3, the activation function used id ReLU .
+MaxPooling Layers: for reducing the spatial dimensions to a size of 2x2.
+Flatten: converts the multi-dimensional output of previous layers into a one-dimensional vector for input into fully connected layers.
+Dropout Layer: to prevent overfitting with a thinning rate of 0.5 after the first Dense layer.
+Dense Layer: last layer of dense for classification with a sigmoid activation function.
+
+Training Details
+The model is trained using binary cross-entropy loss and the Adam optimizer. It is validated with 20% of the training data reserved for validation.
+The model employs 4-fold cross-validation to ensure robust performance.
+The following callbacks are used during training:
+EarlyStopping: Stops training if the validation accuracy ceases to improve for a specified patience period.
+ModelCheckpoint: Saves the best weights during training based on validation accuracy.
+The best-performing model from each fold is saved, and the model with the best weights overall is selected for final testing.
+
+Performance Evaluation
+After training, the model is evaluated on the test set. The following metrics are used to measure performance:
+Accuracy: The percentage of correct classifications.
+Precision, Recall, F1-Score: For evaluating the model’s classification ability on both real and AI-generated images.
+Confusion Matrix: Displays true positives, false positives, true negatives, and false negatives.
+Instructions
+
+To run the project
+Place the images in the respective training and testing folders.
+Preprocess the images by resizing and normalizing them.
+Train the model using the provided code.
+Evaluate the model on the test set.
 
+Visualization results 
+Confusion Matrix: To visualize the classification performance.
+Training and Validation Metrics: Plots for accuracy and loss over the epochs.