Support for YOLO-Worldv2 models alongside currently supported YOLO-like ONNX model files

[TimC2225]

Preliminary Checks

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• This issue is not a question, bug report, or anything other than a feature request directly related to this project.

Proposal

Support for YOLO-Worldv2 models could be added to the "Custom Object Detection with YOLO-like ONNX model file" feature. I have tried using persisting models saved using the following method provided by Ultralytics, but it fails to detect any objects when launching display_zed_cam.launch.py. The PT file saved was exported as ONNX through the CLI as given in the README.

from ultralytics import YOLO

# Initialize a YOLO-World model
model = YOLO("yolov8s-worldv2.pt")  # or select yolov8m/l-worldv2.pt

# Define custom classes
model.set_classes(["person", "bus"])

# Save the model with the defined offline vocabulary
model.save("custom_yolov8s.pt")

Ultralytics' documentation for YOLO-World states that the models saved using the above method behave like any other pre-trained YOLOv8 model, so I am unsure if the saved persisting YOLO-Worldv2 models should already work with the ZED ROS2 wrapper once exported as ONNX.

Use-Case

This would allow persisting YOLO-World models to display objects detected through the ROS 2 plugin used to visualize the results of the Object Detection, hopefully making zero-shot detection with dynamic custom classes possible with the ZED stereo cameras.

Anything else?

No response

[Myzhar]

Hi @TimC2225
can you try if your model works using native ZED SDK?
You can use these examples: https://github.com/stereolabs/zed-sdk/tree/master/object%20detection/custom%20detector

[TimC2225]

From my previous experience with the detector.py example, the YOLO-Worldv2 models work using the native ZED SDK.