Why does my machine learning model always overfit on my dataset, and how can I prevent this from happening in the future?

I totally understand your frustration, overfitting can be a real challenge. Have you considered looking at the size of your training dataset? If it's too small, your model might be memorizing the training data rather than learning generalizable patterns. You might want to try data augmentation techniques like rotation, flipping, or color jittering to artificially increase the size of your dataset.

Another thing to check is the complexity of your model. If it's too powerful, it might be overfitting to the noise in your training data. You could try simplifying your model by reducing the number of layers or the number of neurons in each layer. Regularization techniques like dropout can also help prevent overfitting by randomly dropping out neurons during training.

I'd also recommend checking your validation accuracy during training. If it's not improving or is even decreasing, that's a sign that your model is overfitting. You can try using early stopping to stop training when the validation accuracy plateaus. Additionally, you could try using a more robust evaluation metric, such as cross-validation or a more robust loss function like mean squared error instead of categorical cross-entropy.

One more thing to consider is whether your dataset is representative of the real-world data your model will be applied to. If it's not, your model might be learning patterns that are specific to your dataset rather than generalizable patterns. You might want to try collecting more data or using transfer learning to leverage pre-trained models that have been trained on larger, more diverse datasets.