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people-segmentation

Code for the model to segment people at the image

https://github.com/ternaus/people_segmentation

Science Score: 44.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ✓
    CITATION.cff file
    Found CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ✓
    .zenodo.json file
    Found .zenodo.json file
  • ○
    DOI references
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    Academic publication links
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    Committers with academic emails
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    Institutional organization owner
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    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (6.4%) to scientific vocabulary

Keywords

computer-vision deep-learning image-segmentation people-segmentation python semantic-segmentation
Last synced: 6 months ago · JSON representation ·

Repository

Code for the model to segment people at the image

Basic Info
Statistics
  • Stars: 132
  • Watchers: 6
  • Forks: 23
  • Open Issues: 2
  • Releases: 2
Topics
computer-vision deep-learning image-segmentation people-segmentation python semantic-segmentation
Created over 5 years ago · Last pushed almost 3 years ago
Metadata Files
Readme Funding License Citation

README.md

Binary segmentation of people

Installation

pip install -U people_segmentation

Example inference

Jupyter notebook with the example: Open In Colab

Data

Train set:

  • Mapillary Vistas Commercial 1.2 (train)
  • COCO (train)
  • Pascal VOC (train)
  • Human Matting

Validation set:

  • Mapillary Vistas Commercial 1.2 (val)
  • COCO (val)
  • Pascal VOC (val)
  • Supervisely

To convert datasets to the format:

``` training coco mattinghumans pascalvoc vistas

validation coco pascal_voc supervisely vistas ``` use this set of scipts.

Training

Define the config.

Example at people_segmentation/configs

You can enable / disable datasets that are used for training and validation.

Define the environmental variable TRAIN_PATH that points to the folder with train dataset.

Example: bash export TRAIN_PATH=<path to the tranining folder>

Define the environmental variable VAL_PATH that points to the folder with validation dataset.

Example: bash export VAL_PATH=<path to the validation folder>

Training

python -m people_segmentation.train -c <path to config>

You can check the loss and validation curves for the configs from people_segmentation/configs at W&B dashboard

Inference

bash python -m torch.distributed.launch --nproc_per_node=<num_gpu> people_segmentation/inference.py \ -i <path to images> \ -c <path to config> \ -w <path to weights> \ -o <output-path> \ --fp16

Web App

https://peoplesegmentation.herokuapp.com/

Code for the web app: https://github.com/ternaus/peoplesegmentationdemo

Owner

  • Name: Vladimir Iglovikov
  • Login: ternaus
  • Kind: user
  • Location: San Francisco

Founder and CEO at Albumentations.AI, Ph.D. in Physics., Kaggle GrandMaster, Co-creator of Albumentations.

Citation (CITATION.cff) 

cff-version: 1.2.0
message: "If you use this software, please cite it as below."
authors:
  - family-names: Vladimir
    given-names: Iglovikov
    orcid: https://orcid.org/0000-0003-2946-5525
title: "People Segmentation using UNet"
version: 0.0.4
doi: 10.5281/zenodo.7708627
date-released: 2020-10-14
url: https://github.com/ternaus/people_segmentation

GitHub Events

Total
  • Watch event: 9
  • Fork event: 1
Last Year
  • Watch event: 9
  • Fork event: 1

Committers

Last synced: 11 months ago

All Time
  • Total Commits: 18
  • Total Committers: 1
  • Avg Commits per committer: 18.0
  • Development Distribution Score (DDS): 0.0
Past Year
  • Commits: 0
  • Committers: 0
  • Avg Commits per committer: 0.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
Vladimir Iglovikov i****v@g****m 18

Issues and Pull Requests

Last synced: 7 months ago

All Time
  • Total issues: 4
  • Total pull requests: 7
  • Average time to close issues: 10 days
  • Average time to close pull requests: 6 minutes
  • Total issue authors: 4
  • Total pull request authors: 1
  • Average comments per issue: 0.5
  • Average comments per pull request: 0.14
  • Merged pull requests: 7
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 0
  • Pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 0
  • Pull request authors: 0
  • Average comments per issue: 0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
  • Tetsujinfr (1)
  • lkp520 (1)
  • RuoyuFeng (1)
  • JohanValero (1)
Pull Request Authors
  • ternaus (7)
Top Labels
Issue Labels
Pull Request Labels

Packages

  • Total packages: 1
  • Total downloads:
    • pypi 257 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 1
  • Total versions: 4
  • Total maintainers: 1
pypi.org: people-segmentation

High quality model for people segmentation.

  • Versions: 4
  • Dependent Packages: 0
  • Dependent Repositories: 1
  • Downloads: 257 Last month
Rankings
Stargazers count: 6.7%
Dependent packages count: 7.3%
Forks count: 9.1%
Average: 11.9%
Downloads: 14.0%
Dependent repos count: 22.1%
Maintainers (1)
ternaus
Last synced: 6 months ago

Dependencies

requirements.txt pypi
  • albumentations *
  • iglovikov_helper_functions *
  • pytorch_lightning *
  • pytorch_toolbelt *
  • segmentation-models-pytorch *
  • torch *
  • tqdm *
.github/workflows/ci.yml actions
  • actions/cache v1 composite
  • actions/checkout v2 composite
  • actions/setup-python v1.1.1 composite
pyproject.toml pypi
setup.py pypi