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:
import segmentation_models_pytorch as smp
model = smp.from_pretrained("smp-hub/segformer-b5-640x640-ade-160k")or with any other encoder:
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.
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:
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.tomlby @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
- Updating the tutorial file by @ytzfhqs in #907
- Example on how to save and load model along with Albumentations preprocessing by @qubvel in #914
- Add open-in-colab badge for all example notebooks by @qubvel in #915
- Switch to pyproject.toml by @adamjstewart in #917
- Remove dep on mock by @adamjstewart in #919
- [feat] Adding camvid segmentation multiclass as an example by @TimbusCalin in #922
- Ruff: format Jupyter notebooks too by @adamjstewart in #923
- Remove docker files by @adamjstewart in #925
- Test minimum and maximum supported dependencies by @adamjstewart in #918
- Test on Linux/macOS/Windows for all supported Python versions by @adamjstewart in #930
- Modify Jaccard, Dice and Tversky losses by @zifuwanggg in #927
- [feat] Adding UPerNet by @brianhou0208 in #926
- Fix dims=None in loss by @qubvel in #937
- Test PR docs build and update models.rst by @qubvel in #943
- Update test_models.py by @brianhou0208 in #940
- Fix UPerNet decoder typo by @brianhou0208 in #945
- Fix Metric typo by @brianhou0208 in #966
- Expose timm constructor arguments by @DimitrisMantas in #960
- fix(examples): correct Colab links by @EDM115 in #965
- Update DeepLab models by @DimitrisMantas in #959
- [feat] Adding SegFormer by @brianhou0208 in #944
- Update MixVisionTransformer by @brianhou0208 in #975
- silance
"is" with 'str' literalsyntax warning frompretrainedmodelsin python >= 3.12 by @YoniChechik in #987 - Fix DeepLabV3Plus encoder depth by @munehiro-k in #986
- Fix style by @qubvel in #989
- Add onnx tutorial by @qubvel in #990
- Fix Segformer decoder performance by @brianhou0208 in #998
- Add description for non-MIT licensed codes by @junkoda in #1000
- Fix encoder depth & output stride on DeeplabV3 & DeeplabV3+ by @brianhou0208 in #991
- Update PAN Decoder support encoder depth by @brianhou0208 in #999
- Update timm universal (support transformer-style model) by @brianhou0208 in #1004
- Refactor tests by @qubvel in #1011
- Dependencies: packaging required for testing by @adamjstewart in #1013
- chore (ci): adopt astral-sh actions by @johnnv1 in #1014
- chore (segformer): move decoder converter scripts by @johnnv1 in #1017
New Contributors
- @adamjstewart made their first contribution in #917
- @TimbusCalin made their first contribution in #922
- @zifuwanggg made their first contribution in #927
- @brianhou0208 made their first contribution in #926
- @DimitrisMantas made their first contribution in #960
- @EDM115 made their first contribution in #965
- @YoniChechik made their first contribution in #987
- @munehiro-k made their first contribution in #986
- @junkoda made their first contribution in #1000
- @johnnv1 made their first contribution in #1014
Full Changelog: v0.3.4...v0.4.0
