Deep Learning for Binary Image Classification
Leveraging AI and Transfer Learning to Automate Complex Visual Tasks with ML.NET
Leveraging AI and Transfer Learning to Automate Complex Visual Tasks with ML.NET
Binary Image Classification is a vital application of deep learning in the field of computer vision. This project focuses on building a high-performance binary image classification model using ML.NET, a powerful machine learning framework for .NET developers. By leveraging transfer learning and a pre-trained ResNet v2 model, this project demonstrates how to effectively automate the visual inspection process, such as identifying structural damage in bridge decks.
Transfer learning allows us to utilize the extensive knowledge embedded in a pre-trained model, drastically reducing the training time while ensuring high accuracy. The use of ML.NET makes this approach accessible and efficient for developers working within the .NET ecosystem.
The project begins with preparing the dataset, where images are resized, normalized, and labeled. The images are split into training, validation, and test sets to ensure a robust evaluation of the model.
The model is based on the 101-layer ResNet v2 architecture, renowned for its depth and ability to capture intricate features in images. The pre-trained layers of ResNet are used as the foundation, with only the final classification layers being retrained to distinguish between the two target classes.
The training process is divided into two phases:
The model's performance is evaluated using key metrics such as accuracy, precision, recall, and F1 score. On the test dataset, the model achieved a remarkable accuracy, showcasing its capability to reliably differentiate between the two target classes. The results are indicative of the model's effectiveness in practical scenarios, where accurate image classification is critical.
The trained model is not only efficient but also scalable, making it suitable for deployment in various environments, from infrastructure monitoring to medical diagnostics.
This project highlights the power and flexibility of using deep learning and transfer learning techniques in binary image classification tasks. By leveraging ML.NET and the ResNet v2 architecture, developers can create efficient and accurate models for complex visual tasks. The model developed in this project is a testament to the potential of AI in automating and improving decision-making processes in critical areas such as infrastructure and healthcare.
Future work could involve expanding the model to handle multi-class classification tasks or integrating it into real-time systems for continuous monitoring and alerting.
Follow these steps to get started with the project: