An industrial company is leveraging a custom deep neural network model developed with TensorFlow to enhance its quality control system. The model is designed to identify semi-finished products that should be discarded, using images captured from various stages of the production line. Despite the model showing convergence during training, the performance on the test set is not meeting expectations. The company is constrained by a tight budget and requires a solution that is scalable across multiple production lines. Given these constraints, which of the following strategies would most effectively address the model's performance issues? (Choose 3 options)