Why the performance of VGG-16 is better than Inception V3?

Question

I’ve initialised VGG16 and InceptionV3 with ImageNet weights and fine tuned using very small learning rate from the first layer on a chest X-ray dataset of 20K images (since the medical images are different from ImageNet classes). I reportedly find the VGG16 shows no overfitting and is more accurate than InceptionV3 that overfits and gives less accurate results. What is the reason?

Answer 1

What are the cons? It has so many weight parameters, the models are very heavy, 550 MB + of weight size. Which also means long inference time Why not just make the model deeper? More heavier model More training time Vanishing gradient problem

With a given receptive field(the effective area size of input image on which output depends), multiple stacked smaller size kernel is better than the one with a larger size kernel because multiple non-linear layers increases the depth of the network which enables it to learn more complex features, and that too at a lower cost. 3X3 kernels help in retaining finer level properties of the image.