To familiarize participants with the mmSegmentation framework, its capabilities, features, and how it can be utilized for semantic segmentation tasks, including RTS mapping.
Breakdown
Why Use a Framework Instead of Building from Scratch?
Efficiency: Frameworks provide pre-implemented functions and structures, reducing development time.
Reliability: Established frameworks are tested by a broad community, ensuring fewer bugs and issues.
Scalability: Frameworks often come with built-in tools for scaling, such as distributed training.
Community Support: Access to a community of users for troubleshooting, sharing best practices, and updates.
Model Zoo: Availability of pre-trained models and benchmarks, facilitating transfer learning and comparison.
Trade-offs: While frameworks offer many advantages, they might come with a learning curve and may not be as flexible as a custom solution for very specific needs.
Overview of mmSegmentation
What is mmSegmentation and its place within the MM (MMLab) ecosystem
Core Components of mmSegmentation
Datasets and Data Loaders: How mmSegmentation handles data
Models: Overview of built-in architectures (including U-Net and its variants)
Configs: Understanding the configuration system in mmSegmentation
Training and Evaluating Models
Setting up a training configuration: Hyperparameters, dataset paths, etc.
Launching a training session: Commands and best practices
Monitoring training: Losses, metrics, and visualizations
Evaluating models: Tools and metrics available within mmSegmentation
Fine-tuning and Transfer Learning
The importance of transfer learning in semantic segmentation
Using pre-trained models from the mmSegmentation model zoo
Fine-tuning strategies for domain-specific tasks like RTS mapping
Customizing mmSegmentation
Adding custom datasets: Preparing data and integrating it into the framework
Implementing custom model architectures or modifications
Extending functionalities: Plugins, hooks, and more
Exploring Other Frameworks for Diverse Applications
Detectron2: Specialized for object detection and instance segmentation.
Transformers (by Hugging Face): Tailored for NLP tasks with transformer architectures.
Introduction to MMSegmentation
Goal
To familiarize participants with the mmSegmentation framework, its capabilities, features, and how it can be utilized for semantic segmentation tasks, including RTS mapping.
Breakdown