Anchor Box Customization in KerasCV's YOLOv8Detector

[bnina-ayoub]

Short Description

Add support for anchor box customization within the keras_cv.models.YOLOV8Detector class. This would allow users to:

• Specify custom anchor boxes during model creation
• Modify anchor boxes after loading a pre-trained model
• Potentially integrate anchor box optimization techniques (e.g., k-means clustering) directly into the API
• Motivation

Anchor Box Importance: Anchor boxes play a critical role in YOLO's object detection performance, especially for accurately predicting bounding box locations and sizes.
Dataset Specificity: The optimal anchor boxes can vary significantly depending on the dataset's object size and shape distribution.
Current Limitation and its Impact: The current KerasCV YOLOV8Detector lacks a straightforward way to customize anchor boxes. This limitation can lead to suboptimal performance, especially when the pre-trained anchor boxes don't align well with the target dataset.
My Experience: In my current project, I'm facing challenges with high-confidence false positives and inaccurate bounding box predictions, which I strongly suspect are related to a mismatch between the default anchor boxes and the characteristics of my dataset.
Need for Customization: The ability to customize anchor boxes would enable me to fine-tune the model more effectively and potentially resolve these issues, leading to improved accuracy and a more robust object detection system.

Improved Accuracy: Enabling anchor box customization would allow users to fine-tune the model for their specific datasets, potentially leading to significant improvements in bounding box accuracy and overall detection performance.

Flexibility and Control: It would provide users with more control over the model's behavior and allow them to experiment with different anchor box configurations to optimize for their specific needs.

Alignment with Other Frameworks: Many other YOLO implementations offer ways to customize anchor boxes. Adding this feature to KerasCV would bring it in line with these frameworks and enhance its usability for object detection tasks.
Existing Implementations

Other Information

Potential Challenges: Implementing anchor box customization in KerasCV's YOLOV8Detector might require careful consideration of the model's architecture and internal structure. However, the potential benefits in terms of improved accuracy and flexibility make it a valuable addition.
User Feedback: The current lack of anchor box customization has been mentioned by several users in online forums and discussions, highlighting the need for this feature.

Conclusion:

I believe that adding support for anchor box customization would be a significant enhancement to KerasCV's YOLOv8Detector, empowering users to fine-tune their models more effectively and achieve better results on a wider range of object detection tasks.

[matpalm]

yolo v8 is anchor free though?
( specifically it adopts the TOOD approach [1] that first appeared in yolo X ( Aug 2021 ) )

[1] https://arxiv.org/abs/2108.07755