Image Classification Using Transfer Learning and Deep Learning

Deep learning models have demonstrated improved efficacy in image classification since the ImageNet Large Scale Visual Recognition Challenge started 2010. Classification of images has further augmented field computer vision with dawn transfer learning. To train a model on huge dataset demands computational resources and add lot cost to Transfer allows reduce also help avoid reinventing wheel. There are several pretrained like VGG16, VGG19, ResNet50, Inceptionv3, EfficientNet etc which widely used. This paper demonstrates using deep neural network VGG16 is trained from dataset. After obtaining convolutional base model, new built top it for based fully connected network. classifier will use features extracted model.