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Leveraging advanced techniques, the model achieves robust performance in handling complex visual features.\n\nLink of Data : https://www.kaggle.com/datasets/kelixirr/pizza-steak-image-classification-dataset/data\n\n\n\nKey Techniques Employed\n\n- Convolutional Neural Network (CNN)\nImplemented a sophisticated CNN architecture utilizing TensorFlow and Keras, designed to learn intricate hierarchical patterns in the images.\n\n\n- Data Augmentation\nApplied data augmentation techniques to enrich the training dataset, enhancing the model's ability to generalize to various image variations.\n\n\n- Dropout Layer\nIncorporated dropout layers to combat overfitting, ensuring the model's robustness by preventing reliance on specific features.\n\n\n- Model Evaluation\nEvaluated model performance in different scenarios:\nModel 1: Created a baseline CNN model.\nModel 2: Implemented data augmentation and added a dropout layer to observe improvements.\n\nResults \n\n\n- Model 1\nEpoch 5:\nTraining Loss: 0.2933 | Accuracy: 88.40%\nValidation Loss: 0.3474 | Accuracy: 85.40%\n\n\n\n- Model 2 (with Data Augmentation and Dropout)\nEpoch 5:\nTraining Loss: 0.4697 | Accuracy: 78.80%\nValidation Loss: 0.3307 | Accuracy: 87.40%\n\n\n\nThese results showcase the progression and improvements observed in Model 2 after incorporating data augmentation and dropout layers. Model 2 demonstrates enhanced generalization and reduced overfitting, as reflected in the validation accuracy.\n\n\n\n\n\n\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Felmezianech%2Fpizza_steak_imageclassification_cnn","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Felmezianech%2Fpizza_steak_imageclassification_cnn","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Felmezianech%2Fpizza_steak_imageclassification_cnn/lists"}