TensorRT or Tflite or ONNX for EfficientDet using Jetson TX2

Hello, I trained a model using the Tensorflow Object Detection API, then I freeze the model with the lastest checkpoint in the training, this generated a .pb file. Then, I have loaded the frozen model into my Jetson TX2 and performed inference using the trained model. The inference looks great, however, I noticed that the inference time for the trained model using the Jetson TX2 is around 2 FPS. I read that different optimizers can reduce the inference time without affecting the performance of the model, some of the optimers are: TensorRT, TFlite, and ONNX. With these information, the following questions arised:

  • What is the difference of the aforementioned optimizers?
  • How can I know if a model is supported by the optimizers?
  • Which optimizer is more suitable for an application using the Jetson TX2 (I believe that the TensorRT)?

Thank you in advanced for all the support.


1. TensorRT is an GPU-optimized inference engine. TFLite and ONNX is a different model format.

2. Most of frameworks are layer-based.
So you will need to check if all the layers of your model are supported.

Below is TensorRT support matrix for your reference:

3. Yes, it’s recommended to use TensorRT.
You can find a example for deploying a TFOD model with TensorRT below:


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