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.