Dog breed image classification using transfer learning
Whenever I hear the words “transfer learning”, it reminds me of above picture where a big brother lends his hand to pull me up. Transfer learning exemplifies this behavior by learning from one task and allowing it to be used for other task with little bit of fine tuning.
Traditional Machine learning, we always start from scratch. We gather big training dataset, use typical deep learning algo and train all the layers of the model. If you have another use case, you start from scratch again. So each model is trained in isolation with no reuse of any kind. This is resource and cost intensive. The size of dataset on each use case is huge and is nit trivial to create. In transfer learning scenario, we reuse model which was once on huge dataset like Imagenet only once by experts using Convolutional Neural Network. There are several predefined network architecture which are already available
- VGGNET : 1st runner up in imagenet 2014 imagenet
- RESNET: Winner in Imagenet 2015
- Inception: Google. Winner of ILSVRC14
All of these model uses CNN behind the scenes. We then reuse these model, freeze all…