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Setup Config

To set up your configuration, generate a config class based on Config using Hydra. The configuration includes general settings, dataset information, and task-specific info (train, inference, validation).

YOLORichProgressBar provides a rich-based progress bar and logging callback for PyTorch Lightning. It is the standard way to display training progress. Alongside it, setup() from yolo.utils.logging_utils returns the full list of callbacks, loggers, and the output save path derived from your config.

import hydra
from yolo import YOLORichProgressBar
from yolo.config.config import Config
from yolo.utils.logging_utils import setup

@hydra.main(config_path="config", config_name="config", version_base=None)
def main(cfg: Config):
    callbacks, loggers, save_path = setup(cfg, exp_name=cfg.name)

from hydra import compose, initialize
from yolo import YOLORichProgressBar
from yolo.config.config import Config
from yolo.utils.logging_utils import setup

with initialize(config_path="config", version_base=None):
    cfg = compose(config_name="config", overrides=["task=train", "model=v9-c"])

callbacks, loggers, save_path = setup(cfg, exp_name=cfg.name)