Hello
I’m a beginner in DNN. I’m using Jetson Nano.
I managed to do transfer learning on a ResNet-18 model with my custom dataset for object detection. It seems quite straight forward with Pytorch.
What I’m struggling with is the deployment of my model.
My question is simple: Is it possible to deploy the model that I trained in Pytorch and run object detection inference on it? Or do I absolutely need to export it to some other format such as Caffe?
It it is possible to do it in Pytorch, and if so, is there a sample code in Python?
hey thanks for a quick reply smth.
When you say “if you are using Jetson Nano, you can directly run the model trained in PyTorch on the Nano.” what libraries/modules shall I use?
Do you mean I have to use the .pth file without converting it to ONNX? Could you be a bit more specific?
That’s great to know that it’s possible!!
Could you direct me to a specific snippet, piece of code or something written in Python so I can take it from there?
I took the code from Jetson Inference examples, which was meant for transfer learning and used it to train on my dataset.
But I didn’t have any code to run the inference.
I will look into the code you sent me. I really appreciate it!!
Hi Smth. I tried DEEPLABV3-RESNET101. It works very well!
Except this is segmentation, and I don’t know how to build a bounding box around the drawn mask over the detected object.
Could you please show me an example that actually draws the bounding boxes around the detected object?
I just realized that Resnet18 is not for object detection but for image classification.
Since I need Object Detection, I will have to write a new script to train for instance Faster RCNN.