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franva

Hi franva ,
CCM = compact camera module. I don't think it's an issue per se. The reason behind it might also be that color camera just has much higher vertical FOV, so the only way to "fix" this was to crop both color+depth maps.
Thanks, Eirk

franva Thanks again for the PR!

Hello franva ,
I assume this happened because there were no prebuilt wheels for RPI. Version 2.9.0.0 and newer have prebuilt wheels for RPI. Could you update the repo to latest version and try again?
Thanks, Erik

franva We have many more models running on our depthai-experiments, I would say at least 20 additional ones. They are just not supported by the depthai-demo (yet). For ML training tutorials, we also have a set of our own at depthai-ml-training, but we do recommend using Roboflow for training custom object detection models, here's more information on that.
Thanks, Erik

Hi @franva ,

Understood, I guess it is not possible to use the models from Model Zoo simply because that those models require very high resolution which OAK-D does not have.
Thanks for the direct answer.

Yes, that's correct. Some of the models on the model zoo are meant for use in server farms where thousands of TOPS are a minimum amount of processing power.

So the AI landscape in general has a huge variance of processing power. For example many of the larger cloud-AI companies (Google, for example) have likely millions or billions of TOPS available for neural inference.

In the case of OAK-D, we have 1.4 TOPS for neural inference. So models that are intended for cloud-deployment either will not run, or will run too slowly to be useful for real-time applications.

Then Model Zoo is not that helpful. I guess I will have to re-train models with a smaller input size in order to use on OAK-D.

I wouldn't say it's not useful. We use it quite a bit. And here is a list of 12 models that we use fairly often from the model zoo:
https://docs.luxonis.com/en/latest/pages/tutorials/pretrained_openvino/#trying-other-models

I think the more accurate thing to say is that the model zoo is very broad: It has models intended for cloud AI application (where 1,000 TOPS is the minimum compute), for telco-edge (where 100s or TOPS are the minimum), and for more device-edge (₁ TOPS or so).

OAK-D is actually on the embedded side, so device edge or smaller. So the models that are on the model zoo that are intended for cloud AI or telco-edge are not as applicable to OAK-D. But the device-edge (or embedded) ones (liked linked above) are quite relevant.

Thoughts?

Thanks,
Brandon