Hello,

Thanks for this nice toolkit - so far it is working as expected, a part from a few minor issues.

After deploying my mmdetection model using the TensorRT backend, I noticed that the number of detections (bbox_count) is capped at 200, even though max_per_img in my model config has been set to 1000.

It looks like the number of detections is capped by mmdeploy; base_static.py config is used as a base for the tensorrt deploy_cfg.

and this config sets the max number of output boxes.

Increasing the variable max_output_boxes_per_class solves the problem. But naturally, there are other parameters in the model cfg that differ from the ones set in the deploy_cfg. Shouldn't the max_output_boxes_per_class and other relevant variables be set from the model config automatically?

What is the correct approach? According to the documentation, I can use the generic deploy configs included by mmdeploy without modification (eg. configs/mmdet/instance-seg/instance-seg_tensorrt-fp16_dynamic-320x320-1344x1344.py, as I have been using up to this point). Should I make my own deploy_cfg instead?