I’ve been searching around for quite a while now, but can’t find any solution. I’ve been working with a couple of demos with pre-trained networks and they are working fine for me.
The next logical step seemed to train my own objects and see if it would detect them.
I followed the tutorial on Jetson AI Fundamentals - S3E5 - Training Object Detection Models - YouTube and created close too 1000 training images.
First piece I noticed is that the files that the camera-capture tool creates an ImageSets/Main/train.txt whereas the train_ssd.py expects the file to be called default.txt. I could fix that by copying the file over to the right file name.
Then I let it train for about 30 epochs which took quite a while but eventually finished.
Lastly I exported the onnx and tried to run it with the detectnet command:
detectnet --model=models/mymodel/ssd-mobilenet.onnx --labels=models/mymodel/labels.txt \ --input-blob=input_0 --output-cvg=scores --output-bbox=boxes /dev/video1
It would run it’s optimisations in the beginning and finally show the camera image, but didn’t detect my object at all. So how can I find out what went wrong during my training?
I’m sorry I’m very new to the topic, so any documentation on how I can go at debugging would be great.
Thank you very much for helping!