Update README.md

README.md

@@ -1,3 +1,52 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+---
+**Real art vs AI-Generated art image classification**
+
+This project provides a ResNet50 pre-trained model for classifying images as either 'real art' or 'fake art'.
+ResNet50 is a deep convolutional neural network with 50 layers, known for its "residual connections" that help mitigate the vanishing gradient problem.
+It allows training of very deep networks by adding shortcut connections that skip one or more layers, making it highly effective for image classification tasks.
+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.
+
+*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 ResNet50 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%.
+
+*ResNet50 model architecture*
+
+The model is pre-trained on 'ImageNet' that contains a large dataset of more than millions images.
+It applies transfer learning, freezing initial layers of ResNet50, and training only the final layers.
+The final layer, which makes the predictions, is a binary classification layer that uses a sigmoid activation function.
+
+*Training Details*
+
+The model is trained using binary cross-entropy loss and the Adam optimizer.
+The model validates itself during training using 20% of the training data as validation, independent of the test data, to monitor performance and avoid overfitting.
+The model is trained for 5 epochs with a batch size of 32 and employs 4-fold cross-validation to ensure robust performance.
+During each fold, the model's weights are saved after training, allowing for the reuse of the best-performing weights.
+
+*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 art and AI-generated art images.
+Confusion Matrix: Provides insights into classification performance. Displays true positives, false positives, true negatives, and false negatives. 
+
+*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.
+
+
+