Hi @Paul, thanks again for looking into this. We don’t have the same system with @Jack_Schultz. I have a Jetson Nano (4GB) version with the following specs:
NVIDIA NVIDIA Jetson Nano Developer Kit
L4T 32.7.1 [ JetPack 4.6.1 ]
Ubuntu 18.04.6 LTS
Kernel Version: 4.9.253-tegra
CUDA 10.2.300
CUDA Architecture: 5.3
OpenCV version: 4.1.1
OpenCV Cuda: NO
CUDNN: 8.2.1.32
TensorRT: 8.2.1.8
Vision Works: 1.6.0.501
VPI: 1.2.3
Vulcan: 1.2.70
Everything as shipped in the JetPack 4.6.1 SDK image (JetPack SDK 4.6.4 | NVIDIA Developer) That is essentially the latest one in terms of functionality other than security updates (up to v4.6.4).
Do you see any problems with our versions that might cause the issue? Also, should we follow the legacy docs (which we do now as this is where you get redirected by default from the “Implement on Jetson Nano” button) or the new docs and if the latter, is there a special image for us as well?
To help looking into the issue I have tried the deployement with a few other models, and could deploy successfully with face-seg/1, traffic-sing-speedlimit/1, character-detection-iis85/2 (all of these are 2.0 models) while we faced the same issue as described in the first message with object-detection-obkad/5, icon-coglc/1 and carbon-model-2/1 (all of which are 3.0 models). For me the prediction power of the model version on deployability on our Nano is getting more and more clear. Is there a possibility to fall back to 2.0 training somehow for the time being?
Thanks again fro your time, we really like the idea behind roboflow and that you make comuter vision projects possible for the community!