Additional Classes Effect on Model Accuracy using Transfer Learning

• Main Author: Kazan, Baran
• Format: Others
• Language: English
• Published: Högskolan i Gävle, Datavetenskap 2020
• Subjects: artificial intelligence; machine learning; PyTorch; transfer learning; Computer Systems; Datorsystem
• Online Access: http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-33970

This empirical research study discusses how much the model’s accuracy changes when adding a new image class by using a pre-trained model with the same labels and measuring the precision of the previous classes to observe the changes. The purpose is to determine if using transfer learning is beneficial for users that do not have enough data to train a model. The pre-trained model that was used to create a new model was the Inception V3. It has the same labels as the eight different classes that were used to train the model. To test this model, classes of wild and non-wild animals were taken as samples. The algorithm used to train the model was implemented in a single class programmed in Python programming language with PyTorch and TensorBoard library. The Tensorboard library was used to collect and represent the result. Research results showed that the accuracy of the first two classes was 94.96% in training and 97.07% in validation. When training the model with a total of eight classes, the accuracy was 91.89% in training and 95.40 in validation. The precision of both classes was detected at 100% when the model solely had cat and dog classes. After adding six additional classes in the model, the precision changed to 95.82% of the cats and 97.16% of the dogs.