v1.0.0
版本发布时间: 2023-04-06 17:34:45
open-mmlab/mmsegmentation最新发布版本:v1.2.2(2023-12-14 13:04:26)
v1.0.0(04/06/2023)
Highlights
We are excited to announce the release of MMSegmentation v1.0.0 as a part of the OpenMMLab 2.0 project! MMSegmentation v1.0.0 introduces an updated framework structure for the core package and a new section called "Projects". This section showcases a range of engaging and versatile applications built upon the MMSegmentation foundation.
In this latest release, we have significantly refactored the core package's code to make it clearer, more comprehensible, and disentangled. This has resulted in improved performance for several existing algorithms, ensuring that they now outperform their previous versions. Additionally, we have incorporated some cutting-edge algorithms, such as PIDNet and SegNeXt, to further enhance the capabilities of MMSegmentation and provide users with a more comprehensive and powerful toolkit. The new "Projects" section serves as an essential addition to MMSegmentation, created to foster innovation and collaboration among users.
Exciting Features
Inferencer
In this release, we introduce the MMSegInferencer, a versatile API for inference that accommodates multiple input types. The API enables users to easily specify and customize semantic segmentation models, streamlining the process of performing semantic segmentation with MMSegmentation.
Usage:
python demo/image_demo_with_inferencer.py ${IMAGE} ${MODEL} --show --device ${DEVICE}
Optimizations
In addition to new features, MMSegmentation v1.0.0 delivers key optimizations for an enhanced user experience.
PyTorch 2.0 Compatibility
MMSegmentation v1.0.0 is now compatible with PyTorch 2.0, ensuring that users can leverage the latest features and performance improvements offered by the PyTorch 2.0 framework when using MMSegmentation. With the integration of inductor, users can expect faster model speeds. The table below shows several example models:
| Model | Training Speed |
|---|---|
| pspnet_r50-d8 | 34.0% ⬆️ (0.3474 -> 0.2293) |
| segformer_mit-b2 | 7.12% ⬆️ (0.1798 -> 0.1670) |
New Features
New features from v1.0.0rc6 to v1.0.0 include:
- Add Mapillary Vistas Datasets support to MMSegmentation Core Package (#2576)
- Support PIDNet (#2609)
- Support SegNeXt (#2654)
- Support calculating FLOPs of segmentors (#2706)
- Support multi-band image for Mosaic (#2748)
- Support dump segment prediction (#2712)
Bug fix
- Fix format_result and fix prefix param in cityscape metric, and rename CitysMetric to CityscapesMetric (#2660)
- Support input gt seg map is not 2D (#2739)
- Fix accepting an unexpected argument
local-rankin PyTorch 2.0 (#2812)
Documentation
- Add Chinese version of various documentation (#2673, #2702, #2703, #2701, #2722, #2733, #2769, #2790, #2798)
- Update and refine various English documentation (#2715, #2755, #2745, #2797, #2799, #2821, #2827, #2831)
- Add deeplabv3 model structure documentation (#2426)
- Add custom metrics documentation (#2799)
- Add faq in dev-1.x branch (#2765)
New Contributors
- @liuruiqiang made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/2554
- @wangjiangben-hw made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/2569
- @jinxianwei made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/2557
- @KKIEEK made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/2747
- @Renzhihan made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/2765