Data

Tools

Indexes

Applications

Experiments

Open Source Science

https://github.com/cptanalatriste/celebrity-generator

A deep convolutional generative adversarial network (DCGAN) for generating faces.

https://github.com/cptanalatriste/celebrity-generator

Science Score: 10.0%

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

  • ○
    CITATION.cff file
  • ○
    codemeta.json file
  • ○
    .zenodo.json file
  • ○
    DOI references
  • ✓
    Academic publication links
    Links to: arxiv.org
  • ○
    Committers with academic emails
  • ○
    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (6.6%) to scientific vocabulary

Keywords

deep-learning generative-adversarial-network image-generation pytorch
Last synced: 9 months ago · JSON representation

Repository

A deep convolutional generative adversarial network (DCGAN) for generating faces.

Basic Info
  • Host: GitHub
  • Owner: cptanalatriste
  • Language: HTML
  • Default Branch: master
  • Homepage:
  • Size: 1.63 MB
Statistics
  • Stars: 0
  • Watchers: 2
  • Forks: 0
  • Open Issues: 13
  • Releases: 0
Topics
deep-learning generative-adversarial-network image-generation pytorch
Created about 6 years ago · Last pushed over 3 years ago
Metadata Files
Readme

README.md

celebrity-generator

love_island_new_roster

A deep convolutional generative adversarial network (DCGAN) for generating faces, trained over a dataset of celebrity photos.

Getting started

To train the network, be sure to do the following first:

  1. Clone this repository.
  2. Download a pre-processed version of the CelebFaces Attributes Dataset.
  3. Place the dataset files in your cloned copy of the repository.
  4. Make sure you have installed all the Python packages defined in requirements.txt.

Instructions

To explore the training process, you can take a look at the dlnd_face_generation.ipynb jupyter notebook. The network code is contained in the celebrity_generator module.

Owner

  • Name: Carlos Gavidia-Calderon
  • Login: cptanalatriste
  • Kind: user
  • Location: London, United Kingdom
  • Company: @alan-turing-institute

Systems engineer by training, software developer by trade. Research Software Engineer at @alan-turing-institute .

GitHub Events

Total
Last Year

Committers

Last synced: 12 months ago

All Time
  • Total Commits: 13
  • Total Committers: 1
  • Avg Commits per committer: 13.0
  • 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
Carlos G. Gavidia c****c@g****m 13

Issues and Pull Requests

Last synced: 12 months ago

All Time
  • Total issues: 0
  • Total pull requests: 26
  • Average time to close issues: N/A
  • Average time to close pull requests: 5 months
  • Total issue authors: 0
  • Total pull request authors: 1
  • Average comments per issue: 0
  • Average comments per pull request: 0.46
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 26
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
Pull Request Authors
  • dependabot[bot] (24)
Top Labels
Issue Labels
Pull Request Labels
dependencies (24)

Dependencies

requirements.txt pypi
  • Jinja2 ==2.11.1
  • MarkupSafe ==1.1.1
  • Pillow ==7.0.0
  • Pygments ==2.6.1
  • QtPy ==1.9.0
  • Send2Trash ==1.5.0
  • appnope ==0.1.0
  • attrs ==19.3.0
  • backcall ==0.1.0
  • beautifulsoup4 ==4.9.0
  • bleach ==3.1.0
  • boto3 ==1.12.47
  • botocore ==1.15.47
  • certifi ==2020.4.5.1
  • chardet ==3.0.4
  • cycler ==0.10.0
  • decorator ==4.4.2
  • defusedxml ==0.6.0
  • docutils ==0.15.2
  • entrypoints ==0.3
  • future ==0.18.2
  • idna ==2.9
  • importlib-metadata ==1.5.0
  • ipykernel ==5.1.4
  • ipython ==7.13.0
  • ipython-genutils ==0.2.0
  • ipywidgets ==7.5.1
  • jedi ==0.16.0
  • jmespath ==0.9.5
  • joblib ==0.14.1
  • jsonschema ==3.2.0
  • jupyter ==1.0.0
  • jupyter-client ==6.1.2
  • jupyter-console ==6.1.0
  • jupyter-core ==4.6.3
  • kiwisolver ==1.0.1
  • matplotlib ==3.1.3
  • mistune ==0.8.4
  • mkl-fft ==1.0.15
  • mkl-service ==2.3.0
  • nbconvert ==5.6.1
  • nbformat ==5.0.4
  • nltk ==3.4.5
  • notebook ==6.0.3
  • numpy ==1.18.1
  • olefile ==0.46
  • packaging ==20.3
  • pandas ==1.0.3
  • pandocfilters ==1.4.2
  • parso ==0.6.2
  • pexpect ==4.8.0
  • pickleshare ==0.7.5
  • prometheus-client ==0.7.1
  • prompt-toolkit ==3.0.4
  • protobuf ==3.11.3
  • protobuf3-to-dict ==0.1.5
  • ptyprocess ==0.6.0
  • pyparsing ==2.4.6
  • pyrsistent ==0.16.0
  • python-dateutil ==2.8.1
  • pytz ==2019.3
  • pyzmq ==18.1.1
  • qtconsole ==4.7.2
  • requests ==2.23.0
  • requests-toolbelt ==0.9.1
  • s3transfer ==0.3.3
  • sagemaker ==1.56.1
  • scikit-learn ==0.22.1
  • scipy ==1.4.1
  • six ==1.14.0
  • smdebug-rulesconfig ==0.1.2
  • soupsieve ==2.0
  • terminado ==0.8.3
  • testpath ==0.4.4
  • torch ==1.4.0
  • torchvision ==0.5.0
  • tornado ==6.0.4
  • tqdm ==4.45.0
  • traitlets ==4.3.3
  • udacity-pa ==0.2.9
  • urllib3 ==1.25.8
  • wcwidth ==0.1.9
  • webencodings ==0.5.1
  • widgetsnbextension ==3.5.1
  • zipp ==2.2.0