CODEWITHSUNDEEP
pythonpytorchobject-detection
Irochka
Irochka

Reputation: 33

object detection: is object in the photo, python

I am trying to detect plants in the photos, i've already labeled photos with plants (with labelImg), but i don't understand how to train model with only background photos, so that when there is no plant here model can tell me so. Do I need to set labeled box as the size of image?

p.s. new to ml so don't be rude, please) enter image description here

Upvotes: 0

Views: 252

Answers (2)

Hossein
Hossein

Reputation: 26004

In your case, you don't need to do anything in that regard. Just grab the detection data that you created and train your network with it. When it comes to testing, you usually set a threshold for bounding boxes accuracy, because you may get lots of them so you only want the ones with the highest confidence.

Then you get/show the ones with highest bbox accuracies and there your go, you get your detection result and you can do what ever you want like cropping them using the bounding box coordinates you get.

If there are no plants, your network will likely create bboxes with an accuracy below your threshold (very low confidence) and then, you just ignore them.

Upvotes: 0

cbolwerk
cbolwerk

Reputation: 430

I recently had a problem where all my training images were zoomed in on the object. This meant that the training images all had very little background information. Since object detection models use space outside bounding boxes as negative examples of these objects, this meant that the model had no background knowledge. So the model knew what objects were, but didn't know what they were not.

So I disagree with @Rika, since sometimes background images are useful. With my example, it worked to introduce background images.

As I already said, object detection models use non-labeled space in an image as negative examples of a certain object. So you have to save annotation files without bounding boxes for background images. In the software you use here (labelImg), you can use verify image to say that it saves the annotation file of the image without boxes. So it saves a file that says it should be included in training, but has no bounding box information. The model uses this as negative examples.

Upvotes: 2

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