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/WongKinYiu/YOLO.git
cd YOLO
pip install -r requirements-dev.txt
# Make sure to work inside the cloned folder.
Alternatively, If you are planning to make a simple change:
Note: In the following examples, you should replace python yolo/lazy.py with yolo .
pip install git+https://github.com/WongKinYiu/YOLO.git
Note: Most tasks already include at yolo/lazy.py, so you can run with this prefix and follow arguments: python yolo/lazy.py
Train Model
To train the model, use the following command:
python yolo/lazy.py task=train
yolo task=train # if installed via pip
Overriding the
datasetparameter, you can customize your dataset via a dataset config.Overriding YOLO model by setting the
modelparameter to{v9-c, v9-m, ...}.More details can be found at Train Tutorials.
For example:
python yolo/lazy.py 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/lazy.py. To run inference and deploy the model, use:
More details can be found at Inference Tutorials.
python yolo/lazy.py task.data.source=AnySource
yolo task.data.source=AnySource # if installed via pip
You can enable fast inference modes by adding the parameter task.fast_inference={onnx, trt, deploy}.