| # @package _global_ | |
| # specify here default configuration | |
| # order of defaults determines the order in which configs override each other | |
| defaults: | |
| - _self_ | |
| - data: anomaly_clip | |
| - model: anomaly_clip | |
| - callbacks: default | |
| - logger: many_loggers # set logger here or use command line (e.g. `python train.py logger=tensorboard`) | |
| - trainer: default | |
| - paths: default | |
| - extras: default | |
| - hydra: default | |
| # information of object and prompt template | |
| - prompt: default | |
| # experiment configs allow for version control of specific hyperparameters | |
| # e.g. best hyperparameters for given model and datamodule | |
| - experiment: null | |
| # config for hyperparameter optimization | |
| - hparams_search: null | |
| # optional local config for machine/user specific settings | |
| # it's optional since it doesn't need to exist and is excluded from version control | |
| - optional local: default | |
| # debugging config (enable through command line, e.g. `python train.py debug=default) | |
| - debug: null | |
| # task name, determines output directory path | |
| task_name: "train" | |
| # tags to help you identify your experiments | |
| # you can overwrite this in experiment configs | |
| # overwrite from command line with `python train.py tags="[first_tag, second_tag]"` | |
| tags: ["dev"] | |
| # set False to skip model training | |
| train: True | |
| # evaluate on test set, using best model weights achieved during training | |
| # lightning chooses best weights based on the metric specified in checkpoint callback | |
| test: True | |
| # simply provide checkpoint path to resume training | |
| ckpt_path: null | |
| # seed for random number generators in pytorch, numpy and python.random | |
| seed: 2025 | |