This section explains how to train ISLR models using the existing datasets and models.
For examples on how to use the datasets and models in configs, click here.
After you have a config ready, run the following python snippet:
import omegaconf from openhands.apis.classification_model import ClassificationModel from openhands.core.exp_utils import get_trainer cfg = omegaconf.OmegaConf.load("path/to/config.yaml") trainer = get_trainer(cfg) model = ClassificationModel(cfg=cfg, trainer=trainer) model.init_from_checkpoint_if_available() model.fit()
This will automatically do all the setup, and start the training for you!
The best checkpoints will also be dumped based on validation from each epoch.
Feel free to play with the different parameters in the existing configs