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| license: apache-2.0 |
| datasets: |
| - DataScienceProject/Art_Images_Ai_And_Real_ |
| pipeline_tag: image-classification |
| library_name: keras |
| --- |
| **Real art vs AI-Generated art image classification** |
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| 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. |
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| ***Installation instructions*** |
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| 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%. |
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| ***ResNet50 model architecture*** |
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| 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. |
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| ***Training Details*** |
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| 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. |
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| ***Performance Evaluation*** |
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| 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. |
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| ***To run the project*** |
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| 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. |
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| ***Visualization results*** |
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| Confusion Matrix: To visualize the classification performance. |
| Training and Validation Metrics: Plots for accuracy and loss over the epochs. |
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| ***Results*** |
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| Test accuracy = 0.784 |
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| Test loss = 0.48 |
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| Precision = 0.76 |
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| Recall = 0.83 |
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| F1 = 0.79 |
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| *Confusion Matrix:* |
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