I tried a different number of layers (resnet10, resnet18, and resnet50) and different resolutions, but the performance is still low.
I would like to know if there are other parameters in the spec file that could help me improve the performance of MaskRCNN model on Jetson Nano. My spec file looks very much like the one in the link I mentioned above.
I’m also using DeepStream SDK for inference. Any help would be great. Thanks in advance.
Hello, thanks for the reply! I used different image_size arguments. For the result of 2FPS which I meant, I used an image_size of 256x256 with a resnet50 as the backbone. I also used the image_size of 1344x832 (as the tutorial link that I mentioned) and the performance was 0.6FPS on average. Then I thought that maybe there was a parameter or something to change in the spec file that would help to improve performance. I have two questions:
1 - As you mentioned, Figure 4 shows the FPS on Jetson Nano. But the trained model in that tutorial has 91 classes. I am training only one class, which is the “lane” class. Should I expect a difference (in performance) between a MaskRCNN trained model with 91 classes and a MaskRCNN model with only one class (which is my case)?
2 - If this is how the Jetson Nano will perform with MaskRCNN models, is there another segmentation model (more lightweight) that I can use to train and use DeepStream as my “platform” for inference?
Thank you, foi all the help @Morganh! I tested this model on my Jetson Nano and I got 0.53 FPS on average. So it seems like this is the performance of MaskRCNN on Jetson Nano, right? Or am I doing something wrong here?