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Data preprocessing: Process the images by resizing them to a common size, converting them to grayscale or RGB, and normalizing the pixel values.
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Create the validation test from the train test.
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Convolutional neural network: train a convolutional neural network on the preprocessed images to classify them into different categories.
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Cast the problem as self-supervised learning
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Model evaluation: evaluate the performance of the models using metrics such as accuracy, precision, recall, and F1-score.
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Hyperparameter Tuning: fine-tune the hyperparameters of the model to achieve a better performance.