Welcome to RSI-Segmentation’s documentation!

Get Started

• Prerequisites
• Installation

Dataset Preparation

• Prepare datasets

Model Zoo

• Benchmark and Model Zoo
• Model Zoo Statistics

Quick Run

• Train a model
  • Train on a single machine
  • Train with multiple machines
  • Manage jobs with Slurm
• Inference with pretrained models
  • Test a dataset

Tutorials

• Tutorial 1: Learn about Configs
  • Config File Structure
  • Config Name Style
  • An Example of PSPNet
  • FAQ
• Tutorial 2: Customize Datasets
  • Customize datasets by reorganizing data
  • Customize datasets by mixing dataset
• Tutorial 3: Customize Data Pipelines
  • Design of Data pipelines
  • Extend and use custom pipelines
• Tutorial 4: Customize Models
  • Customize optimizer
  • Customize optimizer constructor
  • Develop new components
• Tutorial 5: Training Tricks
  • Different Learning Rate(LR) for Backbone and Heads
  • Online Hard Example Mining (OHEM)
  • Class Balanced Loss
  • Multiple Losses
  • Ignore specified label index in loss calculation
• Tutorial 6: Customize Runtime Settings
  • Customize optimization settings
  • Customize training schedules
  • Customize workflow
  • Customize hooks

Useful Tools and Scripts

• Useful tools
  • Get the FLOPs and params (experimental)
  • Publish a model
  • Convert to ONNX (experimental)
  • Evaluate ONNX model
  • Convert to TorchScript (experimental)
  • Convert to TensorRT (experimental)
• Miscellaneous
  • Print the entire config
  • Plot training logs
  • Model conversion
• Model Serving
  • 1. Convert model from MMSegmentation to TorchServe
  • 2. Build mmseg-serve docker image
  • 3. Run mmseg-serve
  • 4. Test deployment
• Confusion Matrix
  • 1.Generate a prediction result in pkl format using test.py
  • 2. Use confusion_matrix.py to generate and plot a confusion matrix

Notes

• Changelog
  • V0.0.1 (5/1/2022)
• Frequently Asked Questions (FAQ)

Indices and tables

• Index

• Search Page