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  2. To install YOLOv3, you can follow these steps:12345
    1. Clone the YOLOv3 repository: `git clone https://github.com/ultralytics/yolov3`
    2. Change to the cloned directory: `cd yolov3`
    3. Install the required dependencies: `pip install -r requirements.txt`
    4. For normal training and evaluation, you can also use the PyTorch-YOLOv3 repository: `git clone https://github.com/eriklindernoren/PyTorch-YOLOv3`
    5. Enable GPU support by editing the Makefile and compile Darknet.
    Learn more:
    git clone https://github.com/ultralytics/yolov3 # clone cd yolov3 pip install -r requirements.txt # install
    github.com/ultralytics/yolov3
    For normal training and evaluation we recommend installing the package from source using a poetry virtual environment. git clone https://github.com/eriklindernoren/PyTorch-YOLOv3 cd PyTorch-YOLOv3/ pip3 install poetry --user poetry install
    github.com/eriklindernoren/PyTorch-YOLOv3
    Clone repo and install [requirements.txt] (https://github.com/ultralytics/yolov5/blob/master/requirements.txt) in a
    • Python>=3.7.0
    (https://www.python.org/) environment, including
    • PyTorch>=1.7
    (https://pytorch.org/get-started/locally/). ```bash git clone https://github.com/ultralytics/yolov3 # clone cd yolov3 pip install -r requirements.txt # install ```
    ultralytics.github.io/yolov3/

    How to install YOLO V3?

    • Update the libraries sudo apt-get update
    • Export Cuda path export PATH=/usr/local/cuda-10.0/bin$ {PATH:+:$ {PATH}} export LD_LIBRARY_PATH=/usr/local/cuda-10.0/lib64$ {LD_LIBRARY_PATH:+:$ {LD_LIBRARY_PATH}}
    pysource.com/2019/08/29/yolo-v3-install-and-run-y…

    Installing YOLOv3

    • Before installing anything, we advise that you make sure the pip version is at least 3.0. You can check the version with the command pip -V. ...
    viso.ai/deep-learning/yolov3-overview/
     
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