Model Roadmap

Roadmap

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:

M1: Proof of Concept

• 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.

M2: Initial Deployment

• 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.

M3: Expanded Dataset

• 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.

M4: Advanced Features and Security

• 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.

M5: TBD