Download Latest Version Segmentation Models - v0.5.0 source code.tar.gz (4.5 MB)
Email in envelope

Get an email when there's a new version of Segmentation Models

Home / v0.4.0
Name Modified Size InfoDownloads / Week
Parent folder
README.md 2025-01-08 8.4 kB
0
Segmentation Models - v0.4.0 source code.tar.gz 2025-01-08 4.0 MB
0
Segmentation Models - v0.4.0 source code.zip 2025-01-08 4.1 MB
0
Totals: 3 Items   8.1 MB 0

New models

Segformer

contributed by @brianhou0208

Originally, SegFormer is a transformer-based semantic segmentation model known for its simplicity and efficiency. It uses a lightweight hierarchical encoder to capture multi-scale features and a minimal decoder for fast inference.

With segmentation-models-pytorch you can utilize the model with a native Mix Vision Transformer encoder as long as with 800+ other encoders supported by the library. Original weights are also supported and can be loaded as follows:

:::python
import segmentation_models_pytorch as smp

model = smp.from_pretrained("smp-hub/segformer-b5-640x640-ade-160k")

or with any other encoder:

:::python
import segmentation_models_pytorch as smp

model = smp.Segformer("resnet34")

See more checkpoints on the HF Hub.

UperNet

contributed by @brianhou0208

UPerNet (Unified Perceptual Parsing Network) is a versatile semantic segmentation model designed to handle diverse scene parsing tasks. It combines a Feature Pyramid Network (FPN) with a Pyramid Pooling Module (PPM) to effectively capture multi-scale context.

:::python
import segmentation_models_pytorch as smp

model = smp.UPerNet("resnet34")

New Encoders

Thanks to @brianhou0208 contribution 800+ timm encoders are now supported in segmentation_models.pytorch. New modern encoders like convnext, efficientvit, efficientformerv2, hiera, mambaout and more can be used as easy as:

:::python
import segmentation_models_pytorch as smp

model = smp.create_model("upernet", encoder_name="tu-mambaout_small")
# or
model = smp.UPerNet("tu-mambaout_small")

New examples

  • Added example for multi-class segmentation by @TimbusCalin
  • Added example for onnx export by @qubvel

Other changes

  • Project migrated to pyproject.toml by @adamjstewart
  • Better dependency managing and testing (minimal and latest dependencies, linux/windows/mac platforms) by @adamjstewart
  • Better type annotations
  • Tests are refactored for faster CI and local testing by @qubvel

All changes

New Contributors

Full Changelog: https://github.com/qubvel-org/segmentation_models.pytorch/compare/v0.3.4...v0.4.0

Source: README.md, updated 2025-01-08