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TropComplique

mtcnn-pytorch

Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks.

Model DevelopmentArchitectureJupyter Notebook
Open on GitHub
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Created 2017-08-29 · Updated 2026-09-28 · #15232 today
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TropComplique/mtcnn-pytorch is ranked 15,232 with 669 stars.

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TropComplique/mtcnn-pytorch is ranked 15,232 among these 8 peers; QuantClimate/GPJax is ranked 15,231.

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Key parameters and specifications
Key parameters and specifications
ParameterValue
LanguageJupyter Notebook
CategoryModel Development
SubcategoryArchitecture
KeywordsObject detection
Stars669
Forks162
Contributors3
Created date2017-08-29
Updated date2026-09-28
Stars in 1 day0
Stars in 7 days0
Rank15,232
Previous rank15,056
Rank change-176
7-day stars rank14,882
Previous 7-day stars rank14,897
7-day stars rank change15
Total stars rank15,405
Previous total stars rank15,384
Total stars rank change-21
CountryUkraine
README summary

MTCNN implementation of **inference stage** of face detection algorithm described in Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks. Example How to use it Just download the repository and then do this For examples see . 2 Pillow, numpy Credit This implementation is heavily inspired by: pangyupo/mxnet_mtcnn_face_detection

Frequently asked questions

How many stars and forks are recorded?

TropComplique/mtcnn-pytorch has 669 stars and 162 forks.

What is the repository rank?

TropComplique/mtcnn-pytorch is ranked 15,232.

What language and category are recorded?

TropComplique/mtcnn-pytorch has 669 stars, uses Jupyter Notebook and belongs to Model Development.

Which repository is closest in rank?

TropComplique/mtcnn-pytorch is compared with QuantClimate/GPJax, ranked 15,231 with 669 stars.

README

MTCNN

pytorch implementation of inference stage of face detection algorithm described in
Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks.

Example

How to use it

Just download the repository and then do this

from src import detect_faces
from PIL import Image

image = Image.open('image.jpg')
bounding_boxes, landmarks = detect_faces(image)

For examples see test_on_images.ipynb.

Requirements

  • pytorch 0.2
  • Pillow, numpy

Credit

This implementation is heavily inspired by:

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