The aim of this project is to implement the ResNet architecture for 2D image classification using PyTorch and Jupyter notebooks. Our primary focus is to create user-friendly Jupyter notebooks that are easy to use, intuitive, and don't require programming skills to train the model. Our aim is to democratize the use of deep learning algorithms for image segmentation, making it accessible to a wider range of users, regardless of their technical expertise. With our implementation, anyone can train the ResNet50 model with ease and achieve accurate segmentation results.
This Jupyter notebook provides all the necessary code to train a deep learning model for image segmentation. The user only needs to provide the training data. For more detailed explanations of each Jupyter notebook and instructions on how to use them, please refer to our wiki page. However, please note that the wiki page is currently under development and will be available soon.
Requires a gmail account:
Requires a gmail account: