This repository contains template code that that can be used as a starting point for computer vision projects. All frameworks, libraries, and data sets are open source and publicly available. Some common tasks included here are:
- Image Classification
- Object Detection
- Instance segmentation
- Gradient-weighted Class Activation Mapping
The Dentex Challenge 2023 aims to provide insights into the effectiveness of AI in dental radiology analysis and its potential to improve dental practice by comparing frameworks that simultaneously point out abnormal teeth with dental enumeration and associated diagnosis on panoramic dental X-rays. The dataset comprises panoramic dental X-rays obtained from three different institutions using standard clinical conditions but varying equipment and imaging protocols, resulting in diverse image quality reflecting heterogeneous clinical practice. It includes X-rays from patients aged 12 and above, randomly selected from the hospital's database to ensure patient privacy and confidentiality. A detailed description of the data and the annotation protocol can be found on the Dentex Challenge website. The data set is publicly available for download from the Zenodo open-access data repository.
Label Studio is an open-source data labeling tool designed for labeling, annotating, and exploring various data types. The tool also features a robust machine learning interface, which can be utilized for training new models, active learning, supervised learning, and various other training techniques.
For more information on how to use Label Studio, please refer to the Label Studio documentation. You can find installation instructions here and in the documentation of this repository here.