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README.md

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-license: apache-2.0
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+license: apache-2.0
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+Of course. Here is a description for your Hugging Face README file, written in English and formatted with Markdown.
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+Real vs. AI-Generated Image Detection Model
+This repository contains a deep learning model trained to classify images as either real or AI-generated (fake). The model was developed as part of a project on Big Data and leverages transfer learning with the powerful ResNet50 architecture. It achieves a test accuracy of 93.34% on the CIFAKE dataset. 🏆
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+A live, interactive demo of this model is available on Hugging Face Spaces. [(Link to your Hugging Face Space here)]
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+Model Architecture
+The model uses a transfer learning approach, which involves two main components:
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+Base Model: A pre-trained ResNet50 model, with its weights trained on the ImageNet dataset, is used as a feature extractor. Its layers were frozen during training to retain the learned features. 
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+Custom Classifier Head: A custom head was added on top of the base model to perform the final binary classification. It consists of:
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+A 
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+Flatten layer. 
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+A 
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+Dense layer with 512 units and a ReLU activation function. 
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+A 
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+Dropout layer with a rate of 0.5 to prevent overfitting. 
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+A final 
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+Dense output layer with a Sigmoid activation function for binary classification. 
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+Dataset
+The model was trained on the 
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+CIFAKE dataset, which is available on Kaggle.  This dataset contains a large collection of images, split into two classes:
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+REAL: Authentic, real-world photographs.
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+FAKE: Images synthetically generated by AI models.
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+To improve the model's ability to generalize and to prevent overfitting, 
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+data augmentation techniques were applied to the training set, including random flips, rotations, and zooms. 
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+Training & Performance
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+Training Environment: The model was trained on Google Colab using an NVIDIA T4 GPU. 
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+Training Process: The model was trained for 20 epochs using the Adam optimizer and binary_crossentropy loss function. 
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+ModelCheckpoint was used to save the best weights after each epoch. 
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+Final Performance: The final model was evaluated on an unseen test set and achieved the following results:
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+Test Accuracy: 93.34% 
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+Precision (for REAL class): 95% 
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+Recall (for FAKE class): 95% 
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+The model's performance slightly exceeds the 93% accuracy reported in the dataset's base paper, demonstrating its effectiveness. 
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+Confusion Matrix
+The confusion matrix below shows a detailed breakdown of the model's performance on the 20,000 images in the test set.