Transfer-Learning-model

Transfer learning generally refers to a process where a model trained on one problem is used in some way on a second related problem. As a result, transfer learning has the benefit of decreasing the training time for a neural network model and can result in lower generalization error.

There are perhaps a dozen or more top-performing models for image recognition that can be downloaded and used as the basis for image recognition and related computer vision tasks such as: VGG16, LeNet-5, AlexNet, ResNet, Inception, Mobile Net, Dense Net, Comparision. But in here, we just introduce two models (VGG-16 and ResNet 50).