mmit 0.6.0

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

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

Installation

To install mmit:

pip install mmit

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)

To Do List

In the future, we plan to add support for:

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

Awesome Sources

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

• PyTorch
• PyTorch Lightning
• Timm
• SMP