mmit is a python library to build any encoder matched with any decoder for any Computer Vision model.
For a quick overview of mmit, check out the documentation.
Let's take a look at what we have here!
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 mmitQuick 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
