TensorRT Optimization for Tensorflow-Unet-Image-segmentation

AI & Data Science Deep Learning (Training & Inference) TensorRT
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Description

I tried to run my Tensorflow-Keras-Unet Model on Jetson-Nano and Jetson-TX2,
but the inference time was so worse in the range of 1.5 minutes for a single image inference.

the model was trained on a windows machine and works fine in windows and ubuntu18 with an inference time in the range of 10us to 19ms

I tried running the Jetson Boards(Nano&TX2) at Max Performance modes but no response of improvement . Still inference time as 90s to 100s

I tried with TensorRT Optimization Techniques but I couldn’t complete the process as lack of knowledge about the same I couldn’t found a resource to learn about this optimization for the Image Semantic Segmentation Problems (say Unet)

Can You Provide some resources to know about this more or any other methods to improve the inference latency

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Hi,
This looks like a Jetson issue. Please refer to the below samlples in case useful.

For any further assistance, we recommend you to raise it to the respective platform from the below link

Thanks!