The problem statement for face mask detection using Convolutional Neural Networks (CNN) with different optimizers can be summarized as follows:

"Develop a CNN-based model to accurately detect whether a person in an image is wearing a face mask or not. The model will be trained on a dataset of images containing people with and without face masks. The objective is to design and compare the performance of the CNN model using different optimizers such as Stochastic Gradient Descent (SGD), Adam, RMSprop, and others. The performance metrics will include accuracy, precision, recall, and F1 score to evaluate the effectiveness of each optimizer in training the model for accurate face mask detection."

This problem statement involves building a deep learning model using CNN for face mask detection and experimenting with various optimizers to optimize the model's training process.

Certainly! Here's a more detailed problem statement for face mask detection using CNN with different optimizers, broken down into key points:

1. Objective: The primary objective is to develop a robust and accurate face mask detection system using CNN.

2. Dataset: Utilize a diverse dataset consisting of images of individuals with and without face masks, ensuring a balance between positive and negative samples.

3. Model Development: Construct a CNN architecture capable of effectively capturing features relevant to face mask detection, considering factors such as varying angles, lighting conditions, and diverse facial appearances.

4. Optimizer Comparison: Investigate and assess the performance of different optimizers including Stochastic Gradient Descent (SGD), Adam, RMSprop, and others. Evaluate their impact on model convergence, generalization, and efficiency.

5. Hyperparameter Tuning: Explore the effects of fine-tuning hyperparameters such as learning rates, momentum, and batch sizes in conjunction with each optimizer to achieve optimal model performance.

6. Evaluation Metrics: Calculate and compare performance metrics including accuracy, precision, recall, and F1 score for each optimizer, emphasizing their ability to effectively handle the intricacies of face mask detection.

7. Model Interpretability: Ensure the interpretability of the trained models to understand the features driving the predictions, providing insights into the detection process.

8. Result Analysis: Present a thorough analysis of the experimental results, highlighting the strengths and weaknesses of each optimizer in the context of face mask detection.

By addressing these points, the study aims to provide a comprehensive understanding of the impact of different optimizers on the performance of CNN-based face mask detection models.