Rekognition custom labels - multiple vs single label?

Including multiple custom labels in a single model versus creating separate models with single labels can indeed have different performance implications in Amazon Rekognition Custom Labels.

When you include multiple custom labels in a single model, the model learns to distinguish between these labels simultaneously. This approach can be beneficial in several ways:

1. Efficiency: It's more cost-effective and resource-efficient to train and manage a single model with multiple labels rather than multiple separate models.

2. Contextual learning: The model can learn to differentiate between similar objects or scenes, potentially improving overall accuracy.

3. Shared features: The model can leverage shared features across different labels, which might lead to better generalization.

However, there are potential trade-offs to consider:

1. Complexity: As the number of labels increases, the model's task becomes more complex, which could impact performance for individual labels.

2. Competition between labels: In some cases, the model might have to decide between multiple labels for a given region of interest, as you suggested. This could lead to different results compared to single-label models.

3. Threshold adjustments: The confidence thresholds for each label might need to be fine-tuned differently in a multi-label model compared to single-label models.

The performance difference between multi-label and single-label models can vary depending on your specific use case, the similarity between labels, and the quality and quantity of your training data. It's worth noting that Amazon Rekognition Custom Labels is designed to handle multiple labels efficiently, and in many cases, a single multi-label model can perform well.

To determine the best approach for your specific needs, it's recommended to experiment with both multi-label and single-label models using your dataset. Compare the precision and recall metrics for each label in both scenarios to make an informed decision. Keep in mind that the process of building a good model often involves iterations and improvements to achieve desired performance.
Sources
Improving an Amazon Rekognition Custom Labels model - Rekognition
Amazon Rekognition Custom Labels