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I'm trying to collect my own training data set for the image detection (Recognition, yet). Right now, I have 4 classes and 750 images for each. Each images are just regular images of the each classes; however, some of images are blur or contain outside objects such as, different background or other factors (but nothing distinguishable stuff). Using that training data set, image recognition is really bad.

My question is, 1. Does the training image set needs to contain the object in various background/setting/environment (I believe not...)? 2. Lets just say training worked fairly accurately and I want to know the location of the object on the image. I figure there is no way I can find the location just using the image recognition, so if I use the bounding box, how/where in the code can I see the location of the bounding box?

Thank you in advance!

user3779940
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It is difficult to know in advance what features your programm will learn for each class. But then again, if your unseen images will be in the same background, the background will play no role. I would suggest data augmentation in training; randomly color distortion, random flipping, random cropping.

You can't see in the code where the bounding box is. You have to label/annotate them yourself first in your collected data, using a tool as LabelMe for example. Then comes learning the object detector.

prometeu
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  • Thank you very much! I acutally used your method to use color distortion, random flipping and cropping and it worked! – user3779940 Apr 11 '18 at 14:09
  • + for the bounding box, if you annotate them and run on the network during the testing time, you will figure out the bounding box location on the image coordinate. – user3779940 Apr 11 '18 at 14:09