Image Classification - Identify Device States

Help
#1

I captured three states of device (doing POC) -

  1. Breadboard with switch (Label - Found)
  2. Breadboard without switch (Label - Not Found)
  3. No Breadboard (Label - Empty)

With current Image classification, I could manage to predict labels like Empty , but classifying Found or Not Found is not very accurate.
Any advice here.

#2

It might depend on your dataset. Are you using grayscale or RGB? For Grayscale, it might be apt to capture images with the switch visible clearly. Also, is there a bias in your dataset, or are the images in each class unequal? You could tweak with the Mobilenet Version, and see if MobileNet v2 0.35 Model works well.

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#3

@dhruvsheth let me try that,
btw do you know how I can view live classification via url “http://localhost:4911”. It seems it does not work. I just looked at instruction given for raspberry pi.
Thanks,
Atul

#4

Ignore , found URL now.

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#5

@atyadav What’s your project ID? We can take a look and see if we can spot any places to improve your model.

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#6

Thanks for help. Looks like I modified images now.But I really like to see this use case working accurately…I shall ask once I recreate model.