This development roadmap is designed to iteratively enhance capabilities, focusing on improving its accuracy, scalability, and robustness. Each model iteration (M1 through M5) represents a step forward. Here's an overview of the planned roadmap:
- Objective: Establish a foundational AI model using the ViT (Vision Transformer) as a base. The primary aim is to set up the necessary tools and framework for AI development within our project.
- Dataset: 10,000~ images
- Focus: Laying down the groundwork for future iterations by validating the initial model architecture and data processing pipelines.
- Objective: Build upon the proof of concept by increasing the dataset size and refining the model based on initial learnings.
- Dataset: 40,194 images
- Focus: Enhance model accuracy and establish a benchmark for performance improvements in subsequent versions.
- Objective: Further increase the dataset size to improve the model's ability to generalize and accurately identify skin cancer from a wider variety of images.
- Dataset: 837,628 images, 30 Categories
- Focus: Target substantial improvements in model performance, particularly in handling diverse and challenging cases.
- Objective: Implement a new Duel model approach one model processing images and the other a natural language model processing a contextual conversational about these images.
- Dataset: 1M~ Images, TBD Categories
- Focus: Focusing on improving the model with a bigger improved dataset, natural language capabilities and moving to a duel model approach.