Object Detection classification I am currently training a classifier for detecting resistors using TensorFlow Object Detection API. For that, I downloaded resistor images from ImageNet and I am currently labeling those who will be used for training.

However, I found two different kinds of resistors in my training images :




I want to know if it's safe to put them under the same label or it would be better to classify them under two different labels so that the training will be more accurate ?
In general, you want to show your model all types of "resistors" it would see in the real world. 

Is it feasible for you to manually go through your dataset and label each of these images as resistor A or resistor B? Do you care if your model can make this distinction? If the answer to either of these is no, I would just leave them all as one label and train the model. It's possible you don't have enough training data for your model to learn that these are different types of resistors, but you can evaluate that after you train.