Object Recognition Demos
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Some Sipeed demos that should work, I have a couple of Sipeed boards with the same functionality as the M5StickV, so far I have just played around with the demos that run under python (which comes preinstalled on their boards) I am guessing that all of their software also works on the M5StickV, although the screen size difference will have to be dealt with, it might just require adjusting the sensor.set_windowing((224, 224)) along with lcd.draw_string(... statements. (I don't have a M5StickV and don't know if M5 crew has translated demos available)
(The sipeed.com site was just revised, it seems the English support is more limited at the moment than usual)
Here is a new bundle with 7 different demos, if you have difficulty you might consult the individual demos I have worked with, listed below.
MaixPy 0.3 demo firmware package
https://bbs.sipeed.com/t/topic/688
There are 2 demos running under tiny yolo2 that require a stripped down version of micro python to be installed, to make room for the class models.
MaixPy Run 20-classes object detection based on tiny-yolov2 in 30 lines~
https://bbs.sipeed.com/t/topic/683This easier to use? M5StickV translated version speaks the 20 object names.
https://github.com/ksasao/brownieMaixPy run face detection (tiny yolo v2)
https://bbs.sipeed.com/t/topic/600The 2 above demos us the same custom Python, with some minor code tweaking you can load both demos together, by placing one of the 2 models on the SD card.
1000 class object recognition, using MobileNet
Packed into a 2.3Mb model file, scroll down to the topic: Run kmodel on MaixPy
https://bbs.sipeed.com/t/topic/682Here is a video with 782 object images, some are from the Stanford training database, the remainder were hand selected from google Images for high match quality, (stacking the deck with better matches than real world random samples)
https://www.youtube.com/watch?v=mzQinEzUgdY&t=276sHere's a text recognition demo
Train,Convert,Run MNIST on sipeed MAIX in 30mins!
https://bbs.sipeed.com/t/topic/569These demos are previewed here:
https://www.maixhub.com/