I am a newbie in neural network.
Saw this article Object detection with deep learning and OpenCV. These three type of neural network are shortlisted in the article
Found a lot of online resources that help in understanding how the neural network actual works.
As building a neural network from scratch is time consuming and not entirely foolproof to get the desired efficiency.
I came across these articles Transfer learning & The art of using Pre-trained Models in Deep Learning,Transfer Learning and Transfer Learning, where we transfer the learning from an existing pre trained network to train the object which we would desire the network to detect.
All of these pre trained network have been trained on a data from either COCO or ImageNet or PASCAL VOC which contain different categories images.
How should my training set of banana images be?
I need a fixed resolution of the image I feed into the network, so can I even feed a half banana like this for the training? I don't have to remove the background for training. Please correct me if I'm wrong.