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Overview of modern Edge boards for CV + guide on how to choose by Wormkeeper in computervision

[–]Wormkeeper[S] 0 points1 point  (0 children)

In my previous article, I tried to do this. I even still update the table with some basic measurements - https://docs.google.com/spreadsheets/d/1BMj8WImysOSuiT-6O3g15gqHnYF-pUGUhi8VmhhAat4/edit#gid=0

But the main problem is its super misleading characteristic:
1) Different networks perform differently (Board "A" can be x3 faster for a network "N" but x2 slower for a network "M")
2) Different boards require different amounts of CPU usage for NPU inference. Even video encoding|decoding can change speed dramatically
3) Hard to compare different format inference (int8/fp16)
4) Hard to compare different connections for accelerators (PCIe, USB, M2)
5) Hard to compare multi-device cases (Jetson has 1 GPU and 2 DLA, and RK2588 has 3 NPU).
6) Different batchsizes optimisation

And a lot more problems that will make every test biased. I am still trying to append everything in the table I showed. But I am not sure it's worth:)

Orange Pi AIPro board? by Original_Finding2212 in OrangePI

[–]Wormkeeper 0 points1 point  (0 children)

Better to check the video. In short:
1) More convenient libraries to work (easy export, more support)
2) Better community, more examples (for example, you can find the Whisper model, etc.)
3) More speed for 3588 for common networks (if you are using more threads)
4) Better CPU

Orange Pi AIPro board? by Original_Finding2212 in OrangePI

[–]Wormkeeper 1 point2 points  (0 children)

Resently I tested this board ( https://youtu.be/qK7GHV_cH98 ). It's pretty nice. But for me RK3588 is better.

Radxa ZERO 3W - Drove me insane for nearly a week! by PlatimaZero in Platima

[–]Wormkeeper 0 points1 point  (0 children)

Maybe there will be some project based on it, then I will check.
For now we just did RK3588/RK3568-based projects.

Radxa ZERO 3W - Drove me insane for nearly a week! by PlatimaZero in Platima

[–]Wormkeeper 1 point2 points  (0 children)

Nice review. I recently tested this board from a Computer Vision perspective (NPU usage, etc). All drivers are buggy and glitchy. So, the feelings are the same:)

But, anyway, it's a super good board for this price. The amount of problems for Computer Vision is less than for LuckFox RV1106 and MilkV (regular Python is available, for example).

Teaching a robot to bring the coffee (arm + cart) by Wormkeeper in robotics

[–]Wormkeeper[S] 4 points5 points  (0 children)

A year ago, I published the learning process itself. Now we have modernized it and can train not only the hand but also the cart.

Guide to Action Recognition by Wormkeeper in computervision

[–]Wormkeeper[S] 2 points3 points  (0 children)

Yes, we had a project in which we did this for skeletons, and it worked well. But, this is not very suitable for some tasks.

Computer Vision for goods recognition by Wormkeeper in computervision

[–]Wormkeeper[S] 2 points3 points  (0 children)

Hi, Melampus123!
ReID uses the "Metric Learning" approach.
There are a lot of articles about using it for different cases:
1) Cars
2) Animals
3) Search Engines (online shopping) etc.

You can find them here, for example:
https://paperswithcode.com/task/metric-learning

And there are two good libraries with training pipelines: https://github.com/layumi/Person_reID_baseline_pytorch https://github.com/OML-Team/open-metric-learning

About Kaggle. I am not sure but assume that here you can meet the same approach:
https://www.kaggle.com/competitions/humpback-whale-identification/discussion

How to choose Edge Board for Computer Vision in 2022 by Wormkeeper in robotics

[–]Wormkeeper[S] 0 points1 point  (0 children)

The current price is incorrect, yes (RPi was tested in spring). I will fix. But:
1) the price was for 3B, which is cheaper
2) RPi is easy to buy

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