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Quick Start

Note

We expect all customizations to be done primarily by passing arguments or modifying the YAML config files. If more detailed modifications are needed, custom content should be modularized as much as possible to avoid extensive code modifications.

Install YOLO

Clone the repository and install the dependencies:

git clone https://github.com/shreyaskamathkm/yolo.git
cd yolo
make setup
# Make sure to work inside the cloned folder.

This creates a .venv virtual environment (if one doesn't exist), installs all dependencies, and sets up pre-commit hooks. You can override defaults with make setup VENV=myenv PYTHON=python3.11.

Alternatively, for a simple change:

Note

In the following examples, replace python -m yolo with yolo if installed via pip.

pip install git+https://github.com/shreyaskamathkm/yolo.git

Train Model

python -m yolo task=train

yolo task=train  # if installed via pip
  • Override dataset to customize your dataset via a dataset config.
  • Override model to select a backbone: v9-c, v9-m, etc.
  • More details at Train Tutorials.
python -m yolo task=train dataset=AYamlFilePath model=v9-m

yolo task=train dataset=AYamlFilePath model=v9-m  # if installed via pip

Inference & Deployment

Inference is the default task of yolo. More details at Inference Tutorials.

python -m yolo task.data.source=AnySource

yolo task.data.source=AnySource  # if installed via pip

Enable fast inference modes by adding task.fast_inference={onnx, trt, deploy}. See Inference & Deployment for setup instructions.