Note that if you go to Dashboard > Export you can download your full dataset with all labels (you can import it back with the CLI) - useful if you’re going to experiment with removing some data.
FYI we have fixed an issue with memory leakage in the object detection training pipeline, the 137 OOM errors should be a thing of the past!
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Hi!
I’ve started looking at the exact same problem of classifying what kind of pollen a bee is bringing into the hive. It seems to me that this is quite a demanding task computing wise, and as mentioned here and in the object detection tutorial that something like a Raspberry Pi is recommended to use. However it is essential for the project that I’m working on to keep power consumption at a minimum.
Will it be possible to do pollen detection and classification on an nRF5340, or will this task be way too demanding for a board like that?
Mathias
Hello @mathias,
You could run some image classification (not object detection) on the nRF5340 but the images size would have a maximum size of 96x96 using MobileNetV1 otherwise it would be too demanding for this board. So I guess for this project it would be hard to get a good accuracy using this board.
Louis
Thank you so much for your quick response, @louis!
What you’re saying sounds reasonable. Do you think it would be possible to do any sort of image classification with Edge Impulse on the nRF5340, or will MobileNetV1 be my only alternative? I’m quite new to Edge Impulse, but so far it seems to be absolutely awesome, so would really prefer being able to use Edge Impulse. When it comes to MobileNetV1, this is the first time I’ve ever heard about it.
How is your project progressing, @alcab? Have you found any cool and relevant datasets to use, or do you collect most of the data yourself?
I think you would be able to classify whether there is a bee on a picture or not with the nRF52840 but classifying what kind of pollen the bee is bringing into the hive is a complete different story :D.
One thing I have seen previously is to be able to detect the hive occupancy using the sound of the bees, this could be low-power but it probably does not match your use case.
Regards,
I guess what you’re saying is correct. Think I have to look a bit more into the beehive relative use cases for the combination of Edge Impulse and low-power.
Thanks!
Thank you for the advice! I will do that!
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Hello @mathias,
FYI, we recently released FOMO, brand new approach to run object detection models on constrained devices: https://www.edgeimpulse.com/blog/announcing-fomo-faster-objects-more-objects
I thought you might be interesting in seeing how it performs for your use case 
Regards,
Louis
@mathias @alcab and others on this thread - we’ve upped the memory limits for all jobs to be less stringent (they can go over memory limits without being killed immediately) and this should resolve all OOMKilled issues. We’re monitoring actively to see if any others happen and can tweak the limits if that’s the case.