Update README.md

README.md

@@ -10,31 +10,41 @@ datasets:
 
 # Model Card for Model ID
 
-This model is designed for classifying images as either 'real art' or 'fake art' using a Convolutional Neural Network (CNN) combined with Error Level Analysis (ELA). The CNN extracts features from images, and ELA enhances artifacts that help distinguish between real and AI-generated art.
+This model is designed for classifying images as either 'real' or 'fake-Ai generated' using a Convolutional Neural Network (CNN) combined with Error Level Analysis (ELA).
+
+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.
+
 
 ## Model Details
 
 ### Model Description
 
-This model utilizes a CNN architecture augmented with ELA techniques to classify images into two categories: 'real art' and 'fake art'. The CNN is trained to detect patterns and features, while ELA highlights inconsistencies or artifacts that are characteristic of AI-generated images.
+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.
 
+Error Level Analysis (ELA) detects changes in digital images by comparing the differences between the original and a compressed version of the image.
+It highlights areas where alterations may have occurred, making it useful for identifying image tampering.
 
-### Direct Use
+After running  ELA we feed the CNN with the result of comparing original and a compressed version of the same image and then we get the output.
 
-This model can be used to classify images as 'real art' or 'fake art' based on the presence of artifacts and features characteristic of each category.
+### Direct Use
 
+This model can be used to classify images as 'real' or 'fake- Ai generated' based on the presence of anomalies and features characteristic of each category.
 
 
 ### Out-of-Scope Use
 
-The model may not perform well on images outside the scope of art or where the visual characteristics are drastically different from those in the training dataset. It is not suitable for use in other domains like medical imaging or non-artistic visual data.
+The model may not perform well on images outside the scope of art or where the visual characteristics are drastically different from those in the training dataset.
+
+may not detect ai-images from newer diffusion models that has another method of creating ai images.
 
 ## Bias, Risks, and Limitations
 
-Users should be aware of the limitations and potential biases of the model, including the possibility of misclassification for artworks that significantly differ from those in the training set. Continuous evaluation and refinement may be required to maintain accuracy.
+ELA can be very good in detecting tamparing such as photoshop,
+there is many diffusion engines that not tamper the image making the ELA weak against those ai-images.
 
 ### Recommendations
 
+Test this model with different hyperparameters / more CNN layers
 
 ## How to Get Started with the Model
 
@@ -42,14 +52,14 @@ Prepare Data: Organize your images into appropriate folders and preprocess them
 Train the Model: Use the provided code to train the model on your dataset.
 Evaluate: Test the model on a separate set of images to assess performance.
 
-[More Information Needed]
+
 
 ## Training Details
 
 ### Training Data
 
-Dataset: [Link to dataset or description]
-Preprocessing: Images are resized, normalized, and enhanced with ELA to highlight features.
+Dataset: DataScienceProject/Art_Images_Ai_And_Real_
+Preprocessing: Images are resized, image quallity changed , ELA version of this image created.
 
 ### Training Procedure