Question about "Detect" Module in the YOLOv8 nn architecture

[evan0greenup]

1. According to the architecture diagram in the figure, for each given image, it can only detect at most three objects. What if there are more than three objects in the image?

2. How will the "Detect" module output (bbox + c) like when there is no object in the image (for example pure white or pure black)?

3. How will loss function back-propagated when there is no object in image?

[glenn-jocher]

@evan0greenup hello! I'm glad you're taking an interest in the YOLOv8 architecture and its "Detect" module. Let's clarify your concerns:

1. The diagram you're referring to is likely a simplified representation for illustrative purposes. In reality, the "Detect" module in YOLOv8 is capable of detecting many more than three objects in an image. It uses a grid system where each cell in the grid can predict multiple bounding boxes and class probabilities. The actual number of objects that can be detected is not limited to three; it depends on the grid size and the number of anchor boxes used.

2. If there are no objects in an image, the "Detect" module would output bounding boxes with very low confidence scores. In practice, during post-processing, these low-confidence predictions are filtered out, so you would end up with no bounding box predictions for an image with no objects.

3. During training, the loss function takes into account the absence of objects in an image. It does this by penalizing incorrect objectness predictions. If there are no objects, the loss function encourages the model to predict low objectness scores for all potential bounding boxes. This is part of the training process that helps the model learn when not to detect objects.

For more detailed information on how YOLOv8 operates, including its loss functions and detection mechanisms, you can refer to the Ultralytics Docs. Keep exploring, and don't hesitate to reach out if you have more questions! 😊🚀