Exported RKNN/ONNX model only has 1 output class instead of 2

Discussion
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1

I’m using Ultralytics YOLOv11s with two custom classes for object detection and exporting it to ONNX and RKNN formats.
I have followed https://docs.ultralytics.com/integrations/rockchip-rknn/
After conversion, inference on the RKNN based device only detects a single class, and the output tensor shape is (1, 6, 8400) instead of the expected (1, 7, 8400). I’ve validated that the source .pt model correctly reports 2 classes, but both ONNX and RKNN exports drop one class.

Model Details

  • Checkpoint path: path/to/yolo11s_custom.pt
  • Number of classes: 2
  • Input size: 640×640

Code used for exporting -
from ultralytics import YOLO

Load the YOLO11 model

model = YOLO(“custom_yolo11s.pt”)

Export the model to RKNN format

rknn_path = model.export(format=“rknn”, name=“rk3588”)
print(f"RKNN model saved to: {rknn_path}")

Actual Behavior

  • Received ONNX , RKNN and metadata.yaml after conversion
  • Exported ONNX model output shape: (1, 6, 8400)
  • Exported RKNN model output shape: (1, 6, 8400)
  • Only one class is detected during inference on the RK3588 board.

Logs & Metadata

metadata.yaml generated alongside the RKNN model:

model:
name: yolo11s_custom
format: rknn input_shape: [1, 3, 640, 640]
output_shape: [1, 6, 8400]
classes: 2

  • ONNX graph info: shows only 6 channels in the final layer.

Additional Notes

  • Manual ONNX export yields the same issue, so it’s unlikely to be an Ultralytics export bug alone.
  • I’ve confirmed that the PyTorch forward pass on the .pt checkpoint produces a shape (1, 7, 8400).

9. Request
Could you please:

  1. Confirm whether there’s an option/flag in the YOLO-Ultralytics or RKNN export pipeline I’m missing to preserve all classes.
  2. Suggest any workarounds or patches to ensure correct output-channel dimensionality.
  3. Identify if this might be a known issue in RKNN-Toolkit 1.5.0 or Ultralytics YOLO v11.

Thank you for your assistance!