CODEWITHSUNDEEP
pythontensorflowobject-detectionlabeling
Dick
Dick

Reputation: 65

Object Detector Training using TensorFlow

I am hoping someone on here has experience in training object detection models with tensorflow. I am a complete newbie, trying to learn. I ran through a few of the tutorials on the tensorflow site and am now going to try a real world example. I am following the tutorial here. I am at the point where I need to label the images.

My plan is to try to detect scallops, but the images I using have several scallops. Some I wouldn't really be able to tell were scallops are other than the fact I have context that they are likely a scallop because they are next to a mound of other scallops.

My questions are:

  1. Am I better off cutting them out and treating them individually? Or labeling images that have several scallops
  2. When labeling the scallops there are many that might look just like a round rock if I didn't have context of seeing other scallops. Should I still label them?
  3. I am guessing I will also need to find some images with differing backgrounds???.

I know I can experiment to see how the models perform, but labeling these images is a labour intensive task, so I am hoping I can borrow from someones experience who has attempted something similar in the past. Example of one of the images that I am part way through labeling:

enter image description here

Upvotes: 0

Views: 65

Answers (1)

Frederik Bode
Frederik Bode

Reputation: 2744

1) Good question! The answer is easy, you should label the images as the model would see them at inference time. There's no reason to "lie" to your model (by not labeling something), you'll only confuse it. Be truthful, if you see a scallop, label it. If you don't label something, it's like a negative example, which will confuse the model. ==> A: multiple scallops

2) Seems like the model will take images of (many) scallops as input, so it's not a problem that it learns that 'round objects next to a mound of scallops are likely also a scallop', it's even a good thing, because they often are. So, again, be truthful, label everything.

3) That depends, how will you use the model at inference time? Will the images all have the same background then? If yes, you don't need different backgrounds, if no, you do need them.

Upvotes: 1

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