When exporting to TensorRT, number of detections is capped at 200 about mmdeploy HOT 5 CLOSED

Hi, @RunningLeon Could you please handle this issue?

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@tehkillerbee Hi, you could create your custom deploy_cfg file by changing keep_top_k=1000. And max_output_boxes_per_class is controlling max number of detection results for each class.
A simple way is to inherit and overwrite by creating a new deploy config file:

# in mmdeploy/configs/mmdet/detection/new_config.py 
_base_ = ['../_base_/base_tensorrt_dynamic-320x320-1344x1344.py']
# got everything from base config

# overwriting parameters you want to change
codebase_config = dict(
    post_processing=dict(
        max_output_boxes_per_class=200,
        keep_top_k=1000 # update to 1000
    ))

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@RunningLeon Ok, that is a good solution then - similar to how the mmdet configs can inherit from eachother and how values can be overwritten.

FYI, I am working on a model, where only a single class is detected, but with a high number of detections per image (eg. 1000) so this was why I set the max_output_boxes_per_class higher. Previously, I did not change keep_top_k, but I still got more than 100 results.

from mmdeploy.

@RunningLeon Ok, that is a good solution then - similar to how the mmdet configs can inherit from eachother and how values can be overwritten.

FYI, I am working on a model, where only a single class is detected, but with a high number of detections per image (eg. 1000) so this was why I set the max_output_boxes_per_class higher. Previously, I did not change keep_top_k, but I still got more than 100 results.
@tehkillerbee Yes, you're right.
That's because parameters are set based on original model config at first. If nothing found in original model config, then it would be set based on deploy config.
You could take a look at here

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@RunningLeon Thanks for the help, that makes sense.

Closing this issue, as it is not a MMdeploy bug

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