Quality Assuring an Image Data Pipeline with Transfer Learning : Using Computer Vision Methodologies

Abstract: The computer vision field has taken big steps forwards and the amount of models and datasets that are being released is increasing. A large number of contemporary models are the result of extensive training sessions on massive datasets, reflecting a significant investment of time and computational resources. This opens up a new opportunity on utilizing the knowledge from this pre-trained models. It is possible to transfer the knowledge from one domain to a more fine-tuned solution on a custom created dataset, and this can help the field of computer vision to improve rapidly. This project utilizes the pre-trained models ResNet50,ResNet18 and DensNet121, for dealing with the challenge of fine-tuning models on a custom created dataset, that is created of grayscale images. The project’s results show how it’s possible to use a pre-trained model for transferring the learned features from one domain to another. In addition to this, the project included creating a binary classifier that is fine-tuned on a balanced dataset and another classifier that was fine-tuned on an imbalanced dataset.