using pretrained convnet common and highly effective approach to deep learning on small image datasets is to use pretrained network pretrained network is saved network that was previously trained on large datasettypically on large-scale image-classification task if this original dataset is large enough and general enoughthen the spatial hierarchy of features learned by the pretrained network can effectively act as generic model of the visual worldand hence its features can prove useful for many different computervision problemseven though these new problems may involve completely different classes than those of the original task for instanceyou might train network on imagenet (where classes are mostly animals and everyday objectsand then repurpose this trained network for something as remote as identifying furniture items in images such portability of learned features across different problems is key advantage of deep learning compared to many oldershallow-learning approachesand it makes deep learning very effective for small-data problems in this caselet' consider large convnet trained on the imagenet dataset ( million labeled images and , different classesimagenet contains many animal classesincluding different species of cats and dogsand you can thus expect to perform well on the dogs-versus-cats classification problem you'll use the vgg architecturedeveloped by karen simonyan and andrew zisserman in it' simple and widely used convnet architecture for imagenet although it' an older modelfar from the current state of the art and somewhat heavier than many other recent modelsi chose it because its architecture is similar to what you're already familiar with and is easy to understand without introducing any new concepts this may be your first encounter with one of these cutesy model names--vggresnetinceptioninception-resnetxceptionand so onyou'll get used to thembecause they will come up frequently if you keep doing deep learning for computer vision there are two ways to use pretrained networkfeature extraction and fine-tuning we'll cover both of them let' start with feature extraction feature extraction feature extraction consists of using the representations learned by previous network to extract interesting features from new samples these features are then run through new classifierwhich is trained from scratch as you saw previouslyconvnets used for image classification comprise two partsthey start with series of pooling and convolution layersand they end with densely connected classifier the first part is called the convolutional base of the model in the case of convnetsfeature extraction consists of taking the convolutional base of karen simonyan and andrew zisserman"very deep convolutional networks for large-scale image recognition,arxiv ( )licensed to