Registry / ai-ml / mmsegmentation

mmsegmentation

library1.2.2pypypiunverified

MMSegmentation is an open-source semantic segmentation toolbox based on PyTorch, part of the OpenMMLab project. It provides a unified framework for training, testing, and deploying segmentation models, supporting a wide range of architectures (e.g., DeepLabV3+, PSPNet, U-Net) and datasets. Current version is 1.2.2 (Dec 2023), with a major v1.0.0 release marking a structural overhaul from v0.x. Release cadence is irregular, with minor patches every few months.

ai-ml

pip install mmsegmentation==1.2.2

github ↗package ↗homepage ↗

INSTALL

IMPORT

SIG · MMSEGMENTATION

mmsegmentation

ai-mlpythonv1.2.2

Install

12.5s avg

Import

—

Disk

340MB

Pass rate

10/ 10

Env Coverage10 / 10

glibc

3.9–3.13

musl

3.9–3.13

Install & Compatibility

Where this runs

tested against v1.2.2 · pip install

no network on importno background threads

Install × environment matrix

Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux

musl

py 3.10–3.95 runs

installs and imports cleanly · install 0.0s · import 0.000s · 339.6MB

glibc

py 3.10–3.95 runs

installs and imports cleanly · install 12.5s · import 0.000s · 327MB

340MB installed

● package 340MB

Code

Verified usage

Verified import paths — ran on the pinned version, not inferred.

MMSegInferencer

✓ from mmseg.apis import MMSegInferencer

✗ from mmseg.apis import MMSegInferencer

Perform semantic segmentation inference using the high-level inferencer API.

from mmseg.apis import MMSegInferencer import os # Initialize the inferencer with a pretrained model inferencer = MMSegInferencer( model='deeplabv3plus_r18-d8_4xb2-80k_cityscapes-512x1024', device='cuda' if os.environ.get('CUDA_VISIBLE_DEVICES') else 'cpu' ) # Run inference on an image result = inferencer('demo/demo.png') print(result['predictions'].shape)

mim --version

Debug

Known issues

breakingMMSegmentation v1.0.0 is a major rewrite from v0.x. Configs, APIs, and model definitions are not backward-compatible. Do not mix mmseg v1.x with mmcv<2.0.0.

fix

Migrate configs to the new format using mmseg's migration tools or rewrite for v1.x. Use mmcv>=2.0.0.

affects: >=1.0.0

gotchaThe 'inference_model' and 'init_model' functions are imported from 'mmseg.apis', not 'mmseg.models' or 'mmseg.core'. Common mistake leads to ImportError.

fix

Use 'from mmseg.apis import init_model, inference_model'.

affects: >=1.0.0

deprecatedThe 'mmseg.core' module is deprecated in v1.x. Functions like 'inference' and 'segmentation' were moved to mmseg.apis.

fix

Update imports to use mmseg.apis instead of mmseg.core.

affects: >=1.0.0

gotchaWhen using custom datasets, the class palette must be defined correctly in the config. Missing or misordered palette can cause silent mislabeling.

fix

Always define 'palette' in the dataset config and ensure it matches the order of classes in 'classes'.

affects: >=1.0.0

Upgrade

Version history

1.2.2latest on PyPI · released Dec 14, 2023

Audit

Dependencies

mmcvrequiredCore dependency; must match mmsegmentation version (mmcv>=2.0.0 for v1.x)

mmenginerequiredRequired for config and runner infrastructure in mmseg v1.x

Agent activity

9 hits · last 30 days

node

Resources

githubhttps://github.com/open-mmlab/mmsegmentation ↗

packagehttps://pypi.org/project/mmsegmentation/ ↗

homepagehttps://mmsegmentation.readthedocs.io ↗