cvpr-cl-challenge

entry for CL vision workshop under name Real-Deel https://eval.ai/web/challenges/challenge-page/829/overview

Table of Contents

1. Parameters
   • Training
   • Early Stopping
   • Wandb
   • Flags
2. Getting Started with Best Command
   • Best Submission Command
3. Plots of Best Run
4. Acknowledgements
5. References

Parameters

Training

• --learning_rate : float Learning rate of the optimizer
• --weight_decay : float L2 regularization coefficient.
• --max_epochs_per_task : int Maximum number of training epochs per task in SL and max number of training steps for RL.
• --max_epochs_decay_per_task : float decay of max_epochs after each task
• --model_type : str model name to select from models in the models folder
• --optimizer : str optimizer name to select an optimizer
• --scheduler_name: str scheduler name that will work if scheduler flag enabled
• --batch_size: int. batch size used during training.
• --sl_nb_tasks: int. number of tasks for supervised learning track.
• --cl_method_name: str. method name as shown in methods/init.py
• --seed: int. seed for deterministic runs in pytorch
• --output_dir: str. output directory for data and wandb logs.

Early stoppping

on validation:

--early_stop: bool

• --early_stop_patience : int Number of epochs with increasing validation loss after which we stop training.
• --early_stop_decay_per_task : float decay of early stopping after each task

on smooth dark training loss --early_stop_train: bool = False

• --early_stop_train_patience: int = 60

Wandb

• --wandb_logging : bool
• --wandb_api : str
• --wandb_entity : str
• --wandb_project : str
• --wandb_run_name : str

Flags

• --debug_mode : bool enable debug mode for a fast dry run
• --use_scheduler : bool use scheduler

Best SL submission

Best Submission Command

Method = METHODS_MAPPING['der']
hparams = Method.HParams(learning_rate=0.00034,
        max_epochs_per_task=7, max_epochs_decay_per_task=1.0,
        submission=True,
        buffer_size=6000, dark_coefficient=1.2,
        early_stop_train=True, early_stop_train_patience=45,
        bic=True
        )

main.py --cl_method_name=der --learning_rate=0.00034 --max_epochs_per_task=7 --max_epochs_decay_per_task=1.0 --submission=True --buffer_size=6000 --dark_coefficient=1.2 --early_stop_train --early_stop_train_patience=45 --bic

Best Run Plots

Metric	Score
Final/Average Online Performance:	0.80
Final/Average Final Performance:	0.86
Final/Runtime (seconds):	482
Final/CL Score:	0.87

Acknowledgements

• Mostafa ElAraby
• Yann Pequignot

References

[1]P. Buzzega, M. Boschini, A. Porrello, D. Abati, and S. Calderara, “Dark Experience for General Continual Learning: a Strong, Simple Baseline,” Apr. 2020, Accessed: Jun. 01, 2021. [Online]. Available: https://arxiv.org/abs/2004.07211v2

[2]P. Buzzega, M. Boschini, A. Porrello, and S. Calderara, “Rethinking Experience Replay: a Bag of Tricks for Continual Learning,” arXiv:2010.05595 [cs, stat], Oct. 2020, Accessed: Jul. 01, 2021. [Online]. Available: http://arxiv.org/abs/2010.05595