I have trained my model on the following classes (car,bus,bike,truck,lcv,van) with resnet-10 and resnet-18 as well but I am not getting good results, many time I got b-box of bus,truck,lcv,van on a single class like on truck using tlt-infer.
I have seen that model which is used in DS (resnet10.caffemodel -> Primary_detector) has class (car,person,cycle,road-sign) but when I run it on bus, truck, lcv it also detect these class as car. So can I consider car,bus,truck,lcv,van in a single category like vehicle while training detector with resnet-10 and then implement classifier to classify vehicle as bus,car,truck,van,lcv so this is right approach or not ? please help me out.
and please let me know what are the best practices to train resnet-10 model with transfer-learning-toolkit framework to get more accurate results.
what minimum data-size we opt ? what should be the resolution of images ? what should epoch value we need to take ? and any other things which are needed to get accurate result from training . please suggest.
Any help will be appreciated, Thanks.