I am new to deep learning and computer vision. I have a problem where i use yolo algorithm (https://pjreddie.com/) to detect objects. In the original paper, they define the output to recognize 80 classes, but for my problem i just want to recognize human only.
So i change the final layer to only 1 neuron, and do the training process with transfer learning techniques (used pretrained weights for the cases of 80 classes, of course not use the final layer weights and these weights becomes random number for my problems). I feed only human data to the algorithm. However, i realize that after longer training, the model becomes worse. It starts to recognize other objects as human.
I would like to hear any advice from you guys, should i also feed non-human data to the model.