Utilize Google's AutoML Vision to train a model optimized for high recall, ensuring minimal false negatives by automatically adjusting hyperparameters and selecting the best model architecture.

Develop a custom model using Vertex AI Workbench user-managed notebooks, focusing on tripling the dataset size with examples of compliant photos to improve model accuracy.

Employ Google's AutoML Vision to train a model optimized for a high F1 score, balancing the trade-off between false positives and false negatives through automated feature engineering and model selection.

Create a custom model using Vertex AI Workbench user-managed notebooks, emphasizing a threefold increase in non-compliant photo examples to enhance the model's ability to reject such photos.

Combine both approaches by first using AutoML Vision to identify the optimal model architecture and then fine-tuning the model with a custom dataset in Vertex AI Workbench to further improve performance.