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Roughly analogous to the speed, size, and accuracy you see in this table: https://github.com/ultralytics/yolov5#pretrained-checkpoints

Probably ’small’ and ‘nano’. What are the file sizes?

This is one of the better descriptions I know of: https://github.com/MikeLud/CodeProject.AI-Custom-IPcam-Models/pull/3

I’ve been doing this for many years, but still prefer print statements over breakpoints for whatever reason. I’ve been doing all of my testing using a script to run and test the tpu_runner.py code, so I’m not really even that familiar with how things interact within CPAI, and I may not be of...

As far as an IDE, I’ve used Eclipse, BBEdit, Sublime, and most recently VS Code. My Linux machine that I’ve done my Coral development on I’m using Sublime and the default Linux text editor (simple text?). And EMacs sometimes. Or vi if I have to. And sometimes I’ll work on some prototype code...

The Coral TPU code is all in python, although some of the failures may be related to the setup configuration/environment. There really isn’t any hardcore coding that you’d need to do. Just figure out how to make the existing code run more reliably via trial and error. Things like refining the...

It looks like you are attempting to use multi-TPU by default, but something is wrong so it falls back to the old method. If you’re feeling adventurous you could dig into it and figure out what’s wrong and let me know what to change. (But it may be just the installed coral library version, which...

You should be always able to use the multi-TPU implementation, even if you only have one. Basically the single TPU just refers to the old code base and multi TPU the newer code base that does better threading. That should fix the error that you were seeing.

‘Best’ practices is going to be what works best for you. People tend to be pretty happy with the custom IPcam models, but they aren’t really available right now on the TPU. YOLOv8 is the best in theory, but is going to require more compute and so may not be the best for you. Similarly, there may...

Not YOLOv8? I trust your opinion, but am curious why?

I wonder how large of a photo/label pairing dataset you could scrape off of Craigslist or Edmunds? Would probably be more complete in the past ten years.

The easiest way to ‘fix’ the problem you’re seeing would just be to be running the multi-TPU code on your single TPU. Do you see any earlier problems that would show why that’s not working?

Yeah, I wouldn't expect to see much difference between 0.03 FPS and 1.0 FPS as far as analysis load goes. It's going to really only make a difference when everything is moving because it's windy or raining.

Thanks for the report. I haven’t seen that problem before. From the stack trace, it looks like it’s using the original single-TPU code which hasn’t been changed recently (as far as I know.) I’m on my phone now, I’ll take a closer look when I get a chance.

If I'm reading that correctly, that's ~700 frames analyzed (motion triggers) over 6 hours, for around 1 frame every 30 seconds? How many frames does a motion trigger cause to be analyzed?

Good to know. What sort of video stream load are you putting on it? (Average FPS?)

Sorry to hear that. I hope it’s better in the next version.

That would be an API change between BI and CPAI, I’d guess. BI would need to send the info to CPAI in order for it to appear in anything on the CPAI side, and I don’t think it does. I’m not familiar with the details of the API, but I haven’t seen anything like that in there.

The most important part of my calculus ended up being power consumption, which is pretty hard to measure. Running a GPU on a steady stream of video for a year is going to cost a bit. For me, for example, I calculated out that if the card ended up using an extra ~40 watts, I'd spend $100-$200 per...

I’m hoping that the guy finishes this board: https://github.com/magic-blue-smoke/Dual-Edge-TPU-Adapter/blob/main/PCIex4_2xDualTPU_s.jpeg And then we can drop a few duals into a single slot.

I do not have experience. It was something I looked into and it seemed interesting until I learned I needed bifurcation and didn’t have it. With that one in particular you may need 4-way bifurcation, which is even more rare, but as I understand it if you only have 2-way that just means that only...

There is also this one, but your motherboard has to be compatible with bifurcation. https://a.co/d/cEtN1G7

For the original models: https://coral.ai/models/object-detection/ There are really two models (MobileNet & EfficentDet) in your comment in different sizes and variations. Mainly think of the different sizes. Then, for models that don't fit well on one TPU, I've been playing with segmenting...

Here's a feature branch to base any USB work off of that will make it much easier to keep things in sync: https://github.com/seth-planet/CodeProject.AI-Server/tree/usb_debug I wonder if the training set from that link can be adapted to train a YOLOv8 model? You should look into the docs here...

If you’re starting from ‘scratch’ with an existing .tflite file, the first step would be to instal the TPU compiler and converting to that format: https://coral.ai/docs/edgetpu/compiler/ I think the YOLOv8 export takes care of that for you, but it may still be needed if you find your own...

Possibly. I found it has a CPU-only instruction midway through the model so it doesn’t run as well as you’d think it should. Everything after that instruction ends up also being CPU-only. (Unless you’re running multi-TPUs, because then you can run the remaining instructions on the next TPU segment.)

Most models can be used on a Coral TPU but require some extra steps to get them exported as the appropriate .tflite file and then more steps to get them segmented across multiple TPUs. YOLOv8 is relatively easy to export since they have the settings built into their export feature, but it’s not...

Also, the original module code mixed different models under the label of MobileNet, so one thing to check is what the model is that is actually being loaded.

The funny thing about accuracy measurements is there are so many false positives to get rid of also, and everyone has different opinions about the cost of false positives. It also just occurred to me that if anyone is setting up a system for development, it should be based off of a GitHub...

I didn’t write that code, but we are trying to use CPAI model configurations in a way that it hadn’t been originally intended. Both because we are wrapping up a number of different sizes and types of models in the Coral module and because we are trying to only download the models on demand. So...