Welcome to MMEditing’s documentation!

Languages: English | 简体中文

MMEditing is an open-source toolbox for image and video processing, editing and synthesis.

MMEditing supports various foundamental generative models, including:

• Unconditional Generative Adversarial Networks (GANs)

• Conditional Generative Adversarial Networks (GANs)

• Internal Learning

• Diffusion Models

• And many other generative models are coming soon!

MMEditing supports various applications, including:

• Image super-resolution

• Video super-resolution

• Video frame interpolation

• Image inpainting

• Image matting

• Image-to-image translation

• And many other applications are coming soon!

MMEditing is based on PyTorch and is a part of the OpenMMLab project. Codes are available on GitHub.

Documentation

Community

• Contributing guidance
• MMEditing projects

Get Started

• Overview
• Installation
• Quick run

User Guides

• Tutorial 1: Learn about Configs in MMEditing
• Tutorial 2: Prepare datasets
• Tutorial 3: Inference with pre-trained models
• Tutorial 4: Train and test in MMEditing
• Tutorial 5: Using metrics in MMEditing
• Tutorial 6: Visualization
• Tutorial 7: Useful tools
• Tutorial 8: Deploy models in MMEditing

Advanced Guides

• Evaluator [Coming Soon!]
• Data structure in MMEditing [Coming Soon!]
• Data pre-processor [Coming Soon!]
• Data flow in MMEditing [Coming Soon!]

How To

• How to design your own models
  • Overview of models in MMEditing
  • An example of SRCNN
  • An example of DCGAN
  • References
• How to prepare your own datasets
  • Supported Data Format
  • Design a new dataset
• How to design your own data transforms
  • Data pipelines in MMEditing
  • Supported transforms in MMEditing
  • Extend and use custom pipelines
• How to design your own loss functions
  • Introduction to supported losses
  • Design a new loss function
  • Available losses

FAQ

• Frequently asked questions

Dataset Zoo

• Overview
• Preparing Vimeo90K Dataset
• Preparing Vid4 Dataset
• Preparing Unpaired Dataset for CycleGAN
• Preparing REDS Dataset
• Unconditional GANs Datasets
• Preparing DF2K_OST Dataset
• Preparing DIV2K Dataset
• Preparing Composition-1k Dataset
• Preparing CelebA-HQ Dataset
• Preparing Places365 Dataset
• Preparing Paris Street View Dataset
• Preparing Vimeo90K-triplet Dataset
• Preparing Paired Dataset for Pix2pix

Changelog

• Changelog

API Reference

• mmedit.apis.inferencers
  • Package Contents
• mmedit.structures
  • Package Contents
• mmedit.datasets
  • Package Contents
• mmedit.datasets.transforms
  • Package Contents
• mmedit.evaluation
  • Package Contents
• mmedit.visualization
  • Package Contents
• mmedit.engine.hooks
  • Package Contents
• mmedit.engine.optimizers
  • Package Contents
• mmedit.engine.runner
  • Package Contents
• mmedit.engine.schedulers
  • Package Contents
• mmedit.models.base_archs
  • Package Contents
• mmedit.models.base_models
  • Package Contents
• mmedit.models.losses
  • Package Contents
• mmedit.models.data_preprocessors
  • Package Contents
• mmedit.models.editors
  • Package Contents
• mmedit.utils
  • Package Contents

Migration from MMEdit 0.x

• Overview
• Migration of Runtime Settings
• Migration of Model Settings
• Migration of Evaluation and Testing Settings
• Migration of Schedule Settings
• Migration of Data Settings
• Migration of Distributed Training Settings
• Migration of Optimizers
• Migration of Visualization
• Migration of AMP Training

Switch Language

• English
• 简体中文

Indices and tables

• Index

• Module Index

• Search Page