LukeAI

what do you think @BloodAxe ? honestly, without the GPU accelerated NMS, I doubt that yoloNAS is as good as yolov7 on the latency/accuracy curve.

@haritsahm Thanks for your advice! I've been trying to implement what you suggest but it's not quite working for me. Where am I going wrong? I've tried quite a few...

when exporting - just when running that script^

ok, I can try. You can see in the netron screenshot that the output dimensions of the onnx aren't defined, whereas when I export yolov7, they are. I'll have to...

ah ok, that makes sense. How could we add that here?

@philipp-schmidt do you have any idea how to set the names in a similar way? I tried this: ```python batch_size = 1 topk_all = 100 shapes = [batch_size, 1, batch_size,...

> Please post the output of netron with your code applied. have added to above post^

Inspecting the `i` object with `dir()` shows it has the following attributes: `['ByteSize', 'Clear', 'ClearExtension', 'ClearField', 'CopyFrom', 'DESCRIPTOR', 'DiscardUnknownFields', 'Extensions', 'FindInitializationErrors', 'FromString', 'HasExtension ', 'HasField', 'IsInitialized', 'ListFields', 'MergeFrom', 'MergeFromString', 'ParseFromString',...

In yolov7 they do this by directly passing input/output names to the `torch.onnx.export` function https://github.com/WongKinYiu/yolov7/blob/84932d70fb9e2932d0a70e4a1f02a1d6dd1dd6ca/export.py#L159 It looks like super-gradients `convert_to_onnx` *DOES* expose those arguments to us... https://github.com/Deci-AI/super-gradients/blob/a43cfcd70072c7be1231f9183b1a717c136ff657/src/super_gradients/training/models/conversion.py#L169

ok I can confirm doing it with kwargs works: ```python model_path = "yolo_nas_s.onnx" onnx_export_kwargs = { 'input_names' : ['images'], 'output_names' : ["num_dets", "det_boxes", "det_scores", "det_classes"] } models.convert_to_onnx(model=net, input_shape=(3,640,640), post_process=end2end, out_path=model_path,...