Segmentation Models.pytorch Versions Save

Updates

• Pytorch image models (timm) version upgrade to 0.9.2

Updates

• Added Apple's Mobile One encoder from repo (use encoder_name="mobileone_s{0..4}").
• Pytorch image models (timm) version upgrade to 0.6.12 (500+ encoders available)
• Minor typo fixes and docs updates

Breaking changes

• Minimum Python version 3.6 -> 3.7

Thanks @VadimLevin, @kevinpl07, @Abd-elr4hman

Updates

• Added Mix Vision Transformer encoder from SegFormer [official code] [paper]. Use argument encoder_name="mit_b0" (or mit_b1..b5) to create a model.
• Minor typo fixes and docs updates

Updates

• Added smp.metrics module with different metrics based on confusion matrix, see docs
• Added new notebook with training example using pytorch-lightning
• Improved handling of incorrect input image size error (checking image size is 2^n)
• Codebase refactoring and style checks (black, flake8)
• Minor typo fixes and bug fixes

Breaking changes

• utils module is going to be deprecated, if you still need it import it manually from segmentation_models_pytorch import utils

Thanks a lot for all contributors!

Updates

• Universal timm encoder. 400+ pretrained encoders from timm available with tu- prefix. List of available encoders here.
• Minor fixes and improvements.

Updates

• New architecture: MANet (#310)
• New encoders from timm: mobilenetv3 (#355) and gernet (#344)
• New loss functions in smp.losses module (smp.utils.losses would be deprecated in future versions)
• New pretrained weight initialization for first convolution if in_channels > 3
• Updated timm version (0.4.12)
• Bug fixes and docs improvement

Thanks to @azkalot1 @JulienMaille @originlake @Kupchanski @loopdigga96 @zurk @nmerty @ludics @Vozf @markson14 and others!

Updates

• New architecture Unet++ (#279)
• New encoders RegNet, ResNest, SK-Net, Res2Net (#286)
• Updated timm version (0.3.2)
• Improved docstrings and typehints for models
• Project documentation on https://smp.readthedocs.io

Thanks to @azkalot1 for the new encoders and architecture!

Fixes

• Fix pytorch-efficientnet package version in requirements.txt to strict 0.6.3 (#260)

Updates

• New decoders DeepLabV3, DeepLabV3+, PAN
• New backbones (encoders) timm-efficientnet*
• New pretrained weights (ssl, wsl) for resnets
• New pretrained weights (advprop) for efficientnets

And some small fixes.

Thanks @IlyaDobrynin @gavrin-s @lizmisha @suitre77 @thisisiron @phamquiluan and all other contributers!

Updates

1. New backbones (mobilenet, efficientnet, inception)
2. depth and in_channels options for all models
3. Auxiliary classification output

Note!

Model architectures have been changed, use previous versions for weights compatibility!