I am trying to build an image classification using transfer learning of VGG16 model. I acquired very small data set of 200 images for each class and used 10 images as validation(I know the data set is small but the requirement was for controlled environment). The model accuracy on unseen data is not great but the issue is with the probability it returns. The model predicts class with abnormally high accuracy even for wrong prediction ex(1.0,0.,2.8 e15) this is predicted softmax output for 3 classes. Apart from improving accuracy I want the probability to be distributed normally because two object from different classes might be present in the image where model should note the presence of object from different class and give less confident prediction. My question is what is wrong with the model, is it low number of data-set or variations of images in a class(I have included number of different smaller category under single higher class as per requirement example: vehicle contains images of car bike truck) . I am exploring data augmentation to increase number of images but doubtful if that statifies the requirement and solving probability issue. Please help.