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insightface | Face Analysis Project on PyTorch and MXNet | Computer Vision library

 by   deepinsight Python Version: Current License: MIT

 by   deepinsight Python Version: Current License: MIT

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kandi X-RAY | insightface Summary

insightface is a Python library typically used in Artificial Intelligence, Computer Vision, Deep Learning, Pytorch applications. insightface has no bugs, it has no vulnerabilities, it has a Permissive License and it has medium support. However insightface build file is not available. You can install using 'pip install insightface' or download it from GitHub, PyPI.
InsightFace is an open source 2D&3D deep face analysis toolbox, mainly based on PyTorch and MXNet. Please check our website for detail. The master branch works with PyTorch 1.6+ and/or MXNet=1.6-1.8, with Python 3.x. InsightFace efficiently implements a rich variety of state of the art algorithms of face recognition, face detection and face alignment, which optimized for both training and deployment.
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Support
Quality
Quality
Security
Security
License
License
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kandi-support Support

  • insightface has a medium active ecosystem.
  • It has 10048 star(s) with 3360 fork(s). There are 449 watchers for this library.
  • It had no major release in the last 12 months.
  • There are 877 open issues and 784 have been closed. On average issues are closed in 14 days. There are 24 open pull requests and 0 closed requests.
  • It has a neutral sentiment in the developer community.
  • The latest version of insightface is current.
insightface Support
Best in #Computer Vision
Average in #Computer Vision
insightface Support
Best in #Computer Vision
Average in #Computer Vision

quality kandi Quality

  • insightface has 0 bugs and 0 code smells.
insightface Quality
Best in #Computer Vision
Average in #Computer Vision
insightface Quality
Best in #Computer Vision
Average in #Computer Vision

securitySecurity

  • insightface has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
  • insightface code analysis shows 0 unresolved vulnerabilities.
  • There are 0 security hotspots that need review.
insightface Security
Best in #Computer Vision
Average in #Computer Vision
insightface Security
Best in #Computer Vision
Average in #Computer Vision

license License

  • insightface is licensed under the MIT License. This license is Permissive.
  • Permissive licenses have the least restrictions, and you can use them in most projects.
insightface License
Best in #Computer Vision
Average in #Computer Vision
insightface License
Best in #Computer Vision
Average in #Computer Vision

buildReuse

  • insightface releases are not available. You will need to build from source code and install.
  • Deployable package is available in PyPI.
  • insightface has no build file. You will be need to create the build yourself to build the component from source.
  • Installation instructions, examples and code snippets are available.
  • insightface saves you 18984 person hours of effort in developing the same functionality from scratch.
  • It has 93157 lines of code, 3661 functions and 750 files.
  • It has high code complexity. Code complexity directly impacts maintainability of the code.
insightface Reuse
Best in #Computer Vision
Average in #Computer Vision
insightface Reuse
Best in #Computer Vision
Average in #Computer Vision
Top functions reviewed by kandi - BETA

kandi has reviewed insightface and discovered the below as its top functions. This is intended to give you an instant insight into insightface implemented functionality, and help decide if they suit your requirements.

  • Get resnet conv layer .
  • Gets the convolution layer .
  • Assign anchors to the image .
  • Get output for convolution .
  • get the SSH convolution layer
  • get crop image2
  • Main function .
  • Train network .
  • Parse command line arguments
  • Test for bad case .

insightface Key Features

State-of-the-art 2D and 3D Face Analysis Project

Citation

copy iconCopydownload iconDownload

@article{guo2021sample,
  title={Sample and Computation Redistribution for Efficient Face Detection},
  author={Guo, Jia and Deng, Jiankang and Lattas, Alexandros and Zafeiriou, Stefanos},
  journal={arXiv preprint arXiv:2105.04714},
  year={2021}
}

@inproceedings{an2020partical_fc,
  title={Partial FC: Training 10 Million Identities on a Single Machine},
  author={An, Xiang and Zhu, Xuhan and Xiao, Yang and Wu, Lan and Zhang, Ming and Gao, Yuan and Qin, Bin and
  Zhang, Debing and Fu Ying},
  booktitle={Arxiv 2010.05222},
  year={2020}
}

@inproceedings{deng2020subcenter,
  title={Sub-center ArcFace: Boosting Face Recognition by Large-scale Noisy Web Faces},
  author={Deng, Jiankang and Guo, Jia and Liu, Tongliang and Gong, Mingming and Zafeiriou, Stefanos},
  booktitle={Proceedings of the IEEE Conference on European Conference on Computer Vision},
  year={2020}
}

@inproceedings{Deng2020CVPR,
title = {RetinaFace: Single-Shot Multi-Level Face Localisation in the Wild},
author = {Deng, Jiankang and Guo, Jia and Ververas, Evangelos and Kotsia, Irene and Zafeiriou, Stefanos},
booktitle = {CVPR},
year = {2020}
}

@inproceedings{guo2018stacked,
  title={Stacked Dense U-Nets with Dual Transformers for Robust Face Alignment},
  author={Guo, Jia and Deng, Jiankang and Xue, Niannan and Zafeiriou, Stefanos},
  booktitle={BMVC},
  year={2018}
}

@article{deng2018menpo,
  title={The Menpo benchmark for multi-pose 2D and 3D facial landmark localisation and tracking},
  author={Deng, Jiankang and Roussos, Anastasios and Chrysos, Grigorios and Ververas, Evangelos and Kotsia, Irene and Shen, Jie and Zafeiriou, Stefanos},
  journal={IJCV},
  year={2018}
}

@inproceedings{deng2018arcface,
title={ArcFace: Additive Angular Margin Loss for Deep Face Recognition},
author={Deng, Jiankang and Guo, Jia and Niannan, Xue and Zafeiriou, Stefanos},
booktitle={CVPR},
year={2019}
}

Community Discussions

Trending Discussions on insightface
  • Memory leak (CPU's RAM) when using onnxruntime on GPU
Trending Discussions on insightface

QUESTION

Memory leak (CPU's RAM) when using onnxruntime on GPU

Asked 2021-Jul-16 at 11:16

I'm using the Insightface library from Pypi (https://pypi.org/project/insightface/), the source code is here: https://github.com/deepinsight/insightface/blob/master/python-package/insightface/model_zoo/scrfd.py.

When I run it on my GPU there is a severe memory leak of the CPU's RAM, over 40 GB until I stopped it (not the GPU memory).

here is my script:

import insightface
import cv2
import time

model = insightface.app.FaceAnalysis()

# It happens only when using GPU !!!
ctx_id = 0

image_path = "my-face-image.jpg"
image = cv2.imread(image_path)

model.prepare(ctx_id = ctx_id, det_thresh=0.3, det_size=[416, 416])

detector =  model.models["detection"]

for i in range(100000):
    start_t = time.time()
    bboxes, landmarks = detector.detect(image)
    end_t = time.time()
    print('Detection time: {}'.format(end_t - start_t))

print('DONE')

My setup is (inside docker):

  • Docker Base Image - nvidia/cuda:11.0.3-cudnn8-devel-ubuntu18.04
  • Nvidia Driver - 465.27
  • python - 3.6.9
  • insightface==0.3.8
  • mxnet==1.8.0.post0
  • mxnet-cu110==2.0.0a0
  • numpy==1.18.5
  • onnx==1.9.0
  • onnxruntime-gpu==1.8.1

ANSWER

Answered 2021-Jul-15 at 19:14

I managed to solve it with the following setup:

  • Ubuntu-20.04
  • Python-3.8
  • Nvidia-470
  • Cuda-11.3
  • Cudnn-8
  • mxnet==1.8.0.post0
  • onnx==1.9.0
  • onnxruntime-gpu==1.8.1
  • insightface==0.4

Source https://stackoverflow.com/questions/68396613

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install insightface

Please start with our python-package, for testing detection, recognition and alignment models on input images.

Support

Jia Guo, guojia[at]gmail.comJiankang Deng jiankangdeng[at]gmail.comXiang An anxiangsir[at]gmail.comJack Yu jackyu961127[at]gmail.com

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