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bchacks-

https://github.com/paintedw0lf/bchacks-

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
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    DOI references
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    Academic publication links
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    Academic email domains
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    Institutional organization owner
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    JOSS paper metadata
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    Scientific vocabulary similarity
    Low similarity (7.3%) to scientific vocabulary
Last synced: 6 months ago · JSON representation ·

Repository

Basic Info
  • Host: GitHub
  • Owner: PaintedW0lf
  • License: agpl-3.0
  • Language: Python
  • Default Branch: main
  • Size: 5.9 MB
Statistics
  • Stars: 0
  • Watchers: 1
  • Forks: 0
  • Open Issues: 2
  • Releases: 0
Created about 2 years ago · Last pushed almost 2 years ago
Metadata Files
Readme Contributing License Citation

README.md

FACIAL RECOGNITION AI USING PRE-TRAINED Machine MODELS

Pls downlod this seprately before running the software

1) Install python 3.11 or less than * We need it to be 3.11 or lower because that is what is compatable pythorch.

2) Cuda 12.1 (https://developer.nvidia.com/cuda-downloads)

  • you will face problems because Nvidia sucks so look at this (https://forums.developer.nvidia.com/t/windows-10-cuda-installation-failure-solved/64389)
  • Run the follwing commands in the Vs code terminal assuming it is run as an administration
    • conda install numba
    • conda install cudatoolkit

3) Creat python virtual enviorment * Run the follwing command: * python3 -m venv [insert name you want to give it] * close the terminal and then re-opne it to activate or run [name of your venv]\Scripts\activate

4) Install Pytorch using the following link in the venv * https://pytorch.org/ * choose the follwing options: * Stable * Your OS * pip * python * Cuda latest version * Then copy the command and run in in your VS code terminal (assuming VS code is running as administration)

5) Run the following command to install all the things that are needed for YOLO V8. * pip install ultralytics

Pls run the follwing programs before running the code

  • checkifGUP.py

    • it should out put the following
    • True
    • 0
    • The GPU you have

  • run the following the code to run the code to detect the code

    • python detectvideostream.py --weights runs/train/yolopaddet/weights/best.pt --conf 0.25 -img-size 640 --source 0

Owner

  • Name: Vanshika
  • Login: PaintedW0lf
  • Kind: user
  • Company: University of British Columbia

Teaching Assistant at UBC | Pursuing Computer Science and Data Science | Business & Education enthusiast

Citation (CITATION.cff) 

cff-version: 1.2.0
preferred-citation:
  type: software
  message: If you use this software, please cite it as below.
  authors:
  - family-names: Jocher
    given-names: Glenn
    orcid: "https://orcid.org/0000-0001-5950-6979"
  - family-names: Chaurasia
    given-names: Ayush
    orcid: "https://orcid.org/0000-0002-7603-6750"
  - family-names: Qiu
    given-names: Jing
    orcid: "https://orcid.org/0000-0003-3783-7069"
  title: "Ultralytics YOLO"
  version: 8.0.0
  # doi: 10.5281/zenodo.3908559  # TODO
  date-released: 2023-1-10
  license: AGPL-3.0
  url: "https://github.com/ultralytics/ultralytics"

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Dependencies

examples/YOLOv8-ONNXRuntime-Rust/Cargo.toml cargo
docker/Dockerfile docker
  • pytorch/pytorch 2.1.0-cuda12.1-cudnn8-runtime build
pyproject.toml pypi
  • matplotlib >=3.3.0
  • numpy >=1.22.2
  • opencv-python >=4.6.0
  • pandas >=1.1.4
  • pillow >=7.1.2
  • psutil *
  • py-cpuinfo *
  • pyyaml >=5.3.1
  • requests >=2.23.0
  • scipy >=1.4.1
  • seaborn >=0.11.0
  • thop >=0.1.1
  • torch >=1.8.0
  • torchvision >=0.9.0
  • tqdm >=4.64.0