Mmit

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mmit is a python library to build any encoder matched with any decoder for any Computer Vision model.

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For a quick overview of mmit, check out the documentation.

Let's take a look at what we have here!

• Main Features
• Installation
• Quick Start
• To Do List

Main Features <!--Main Features-->

mmit is engineered with the objective of streamlining the construction of Computer Vision models. It offers a consistent interface for all encoders and decoders, thus enabling effortless integration of any desired combination.

Here are just a few of the things that mmit does well:

• Any encoder works with any decoder at any input size
• Unified interface for all decoders
• Support for all pretrained encoders from timm
• Pretrained encoder+decoders modules 🚧
• PEP8 compliant (unified code style)
• Tests, high code coverage and type hints
• Clean code

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Installation <!--Installation-->

To install mmit:

pip install mmit

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Quick Start <!--Quick Start-->

Let's look at a super simple example of how to use mmit:

import torch
import mmit

encoder = mmit.create_encoder('resnet18')
decoder = mmit.create_decoder('unetplusplus') # automatically matches encoder output shape!

x = torch.randn(2, 3, 256, 256)
features = encoder(x)
out = decoder(*features)

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To Do List

In the future, we plan to add support for:

• [x] timm encoders
• [ ] some of timm transformers encoders with feature extraction
• [ ] torchvision / torchub models
• [ ] more decoders
• [ ] lightning script to train models
• [x] multiple heads
• [ ] popular loss function
• [ ] popular datasets
• [ ] popular metrics

Awesome Sources <!-- omit in toc -->

This project is inspired by, and would not be possible without, the following amazing libraries

• PyTorch
• PyTorch Lightning
• Timm
• SMP