# ayoolaolafenwa/PixelLib

Visit PixelLib's official documentation  https://pixellib.readthedocs.io/en/latest/

Repository: https://github.com/ayoolaolafenwa/PixelLib
Canonical: https://ross.abutalabs.com/products/pixellib
Language: Python
License: MIT
License Family: permissive
Topics: computer-vision, machine-learning, artificial-intelligence, image-segmentation, semantic-segmentation, instance-segmentation, video-segmentation, deeplab, deeplearning, maskr-cnn, tensorflow, deep-learning, pointrend, pytorch, convolutional-neural-networks, segmentation, object-detection
Last push: 2023-10-06T19:27:13+00:00

## Health v2 (maintenance only)
Score: 23/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 8, longevity 100
- inputs: {"age_days": 2334, "days_push": 1062, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1072, forks 268 (observed 2026-08-28T04:03:28.418921+00:00)

## What it is
PixelLib is a Python library for performing semantic and instance segmentation of objects in images and videos with just a few lines of code. It supports both PyTorch (PointRend) and TensorFlow (Mask R-CNN, DeepLab) backends, custom model training, and background editing of images and videos.

## Use cases
- segment objects in images with a few lines of code
- perform instance segmentation on videos
- remove or blur video backgrounds
- train a custom image segmentation model
- run semantic segmentation with DeepLab
- do real-time instance segmentation with PointRend

## When to choose
- you need quick image or video segmentation without writing deep learning code
- you want both semantic and instance segmentation from one library
- you need cross-platform segmentation that works on Windows and Linux
- you want to train a custom segmentation model with minimal code

## When to avoid
- you need cutting-edge segmentation architectures beyond PointRend or Mask R-CNN
- you require active development or frequent updates
- you need GPU-optimized production inference at large scale
- you work outside Python or need non-TensorFlow/PyTorch backends

## Facets
- artifact type: library
- maturity: maintenance
- function: computer-vision, image-processing, machine-learning, deep-learning
- domain: computer-vision, image-processing, machine-learning, deep-learning
- platform: python, windows, cross-platform
- tags: image-segmentation, semantic-segmentation, instance-segmentation, video-segmentation, mask-r-cnn, pointrend, tensorflow, pytorch, object-detection, background-removal, video, linux

## Member repositories
- ayoolaolafenwa/PixelLib (main) score 23

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:28.418921+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T06:53:40.620929+00:00, confidence not recorded.
  - readme: https://github.com/ayoolaolafenwa/PixelLib (fetched 2026-08-28T04:03:28.418921+00:00, sha be61ba6b406e)
  - registry_pypi: https://pypi.org/pypi/pixellib/json (fetched 2026-08-29T12:55:55.750996+00:00, sha 68048d22449d)
- Data as of 2026-08-30T08:39:29.467469+00:00.