feat(model): Add YOLO26 support

[richard-xx]

Purpose

Added Yolo26 export support

Specification

None / not applicable

Dependencies & Potential Impact

None / not applicable

Deployment Plan

None / not applicable

Testing & Validation

None / not applicable

[dtronmans]

Hello Richard,

Thank you for your quick contribution by adding YOLO26 support! Since YOLO26 inference is end-to-end and bypasses post-processing steps like DFL and NMS, we opted for pruning the model in a way that takes advantage of the native one-to-one detection heads.

That means that, instead of relying on the previous cv2 and cv3 heads (and cv4 heads for masks and keypoints), we opted for using the new end-to-end one2one_cv2 and one2one_cv3 heads, which required us to add and a new exporter. This also means that we rely on a single, directly regressed output rather than the traditional three outputs from the FPN. Your implementation is completely correct, however using the old heads and three outputs means that NMS still has to be performed, whereas NMS-free inference is an aspect of YOLO26 that we are trying to leverage in our repositories.

For now, a YOLO26 model can be exported through the main branch of this repository, converted on HubAI and run on the main branch of depthai-nodes (in this case, it can be run without changes in the generic-example tutorial). Over the next few weeks, our next release cycle will make the full YOLO26 end-to-end conversion available on HubAI.

Thanks again for your contribution and your efforts, and I’m happy to answer any questions or discuss this further.