|
--- |
|
license: apache-2.0 |
|
datasets: |
|
- DataScienceProject/Art_Images_Ai_And_Real_ |
|
pipeline_tag: 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 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 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 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. |
|
|
|
***Results*** |
|
|
|
Test accuracy = 0.77 |
|
|
|
Test loss = 0.49 |
|
|
|
Precision = 0.77 |
|
|
|
Recall = 0.77 |
|
|
|
F1 = 0.77 |
|
|
|
*Confusion Matrix:* |
|
|
|
|
|
 |
|
|
