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I am doing some cat poop research and I tried to use YoloV5 to detect different types of poop in the litter box. I collected about 130 poop pictures (Just poop pictures with no background) and labeled them and use roboflow to get annotations, then I follow colab notebook to train data and get the best.pt file for detection. When I run detection using a ramdom litter box picture, the rectangle just marked the whole image or half of the image instead of marking the poops in that image. Then I tried to labled 3 litter box images (Marked poops inside the litter box image) and do it all over again. But when I run detection using a litter box image. Nothing happened. I am so confused. Is it because poop shapes and color are so different to one and the other so it caused the detection didn't work. Anyone could give me some clues on how to lable the images and train them? Thank you enter image description here

Frank Zeng
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First i must say that your project is interesting and funny as well, no offence. Your problem must be due to the number of training images. We cant expect the model to detect after training it with 130 images. Experts say we must use at least 1500 images for single class. And some tips for labelling images in roboflow

  1. Draw a box which includes all the parts of your interest. Dont leave any areas.
  2. Try to avoid overlapping areas.
Vijay P
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