Detectnet_v2 Inference without Image Output

When we run “tao-deploy detectnet_v2 inference”, it produces two types of outputs: images (in “images_annotated” directory) and bbox labels.

Saving the image files takes too long time compared to inference itself. I don’t need the annotated image files actually so I would save the time spent for writing. I just need to save the label files so I can process it in later steps.

Is there a way in “detectnet_v2 inference” to achieve this? If not, what “tao-deploy” alternatives would you recommend? Thank you in advance.

There is no update from you for a period, assuming this is not an issue anymore. Hence we are closing this topic. If need further support, please open a new one. Thanks

Officially, you can have a look at GitHub - NVIDIA-AI-IOT/tao-toolkit-triton-apps: Sample app code for deploying TAO Toolkit trained models to Triton and then config the etlt model in it. Then, modify to save labels only.

In other way, you can refer to a standalone way to run inference. For example, Run PeopleNet with tensorrt - #21 by carlos.alvarez

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