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example_run.sh
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68 lines (60 loc) · 16.1 KB
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wandb_name = <Your Wandb Name>
python fetch_wandb_training.py --wandb_name $wandb_name --dataset cifar10 --batch_size 200 --embed_model none --classifier_model clip_ViTB32 --trainer_config flexmatch
python fetch_wandb_training.py --wandb_name $wandb_name --dataset cifar10 --batch_size 1000 --embed_model none --classifier_model clip_ViTB32 --trainer_config flexmatch
python fetch_wandb_training.py --wandb_name $wandb_name --dataset cifar10 --batch_size 200 --embed_model none --classifier_model coca_ViTB32 --trainer_config flexmatch
python fetch_wandb_training.py --wandb_name $wandb_name --dataset cifar10 --batch_size 1000 --embed_model none --classifier_model coca_ViTB32 --trainer_config flexmatch
python fetch_wandb_training.py --wandb_name $wandb_name --dataset cifar10 --batch_size 200 --embed_model clip_ViTB32 --classifier_model linear --trainer_config flexmatch
python fetch_wandb_training.py --wandb_name $wandb_name --dataset cifar10 --batch_size 1000 --embed_model clip_ViTB32 --classifier_model linear --trainer_config flexmatch
python fetch_wandb_training.py --wandb_name $wandb_name --dataset cifar10 --batch_size 200 --embed_model coca_ViTB32 --classifier_model linear --trainer_config flexmatch
python fetch_wandb_training.py --wandb_name $wandb_name --dataset cifar10 --batch_size 1000 --embed_model coca_ViTB32 --classifier_model linear --trainer_config flexmatch
python fetch_wandb_training.py --wandb_name $wandb_name --dataset cifar10 --batch_size 200 --embed_model clip_ViTB32 --classifier_model shallow --trainer_config flexmatch
python fetch_wandb_training.py --wandb_name $wandb_name --dataset cifar10 --batch_size 1000 --embed_model clip_ViTB32 --classifier_model shallow --trainer_config flexmatch
python fetch_wandb_training.py --wandb_name $wandb_name --dataset cifar10 --batch_size 200 --embed_model coca_ViTB32 --classifier_model shallow --trainer_config flexmatch
python fetch_wandb_training.py --wandb_name $wandb_name --dataset cifar10 --batch_size 1000 --embed_model coca_ViTB32 --classifier_model shallow --trainer_config flexmatch
python plot_training.py --wandb_name $wandb_name --dataset cifar10 --batch_size 200 --embed_model none --classifier_model clip_ViTB32 --trainer_config flexmatch --metrics "Pool Accuracy" --strategies random confidence entropy margin coreset galaxy badge --smoothing max --colors 'tab:blue' 'tab:orange' 'tab:green' 'tab:red' 'tab:purple' 'tab:brown' 'tab:pink' --y_lim .965 1
python plot_training.py --wandb_name $wandb_name --dataset cifar10 --batch_size 200 --embed_model none --classifier_model clip_ViTB32 --trainer_config flexmatch --metrics "Test Accuracy" --strategies random confidence entropy margin coreset galaxy badge --smoothing max --colors 'tab:blue' 'tab:orange' 'tab:green' 'tab:red' 'tab:purple' 'tab:brown' 'tab:pink' --y_lim .96 .982
python plot_training.py --wandb_name $wandb_name --dataset cifar10 --batch_size 200 --embed_model clip_ViTB32 --classifier_model linear --trainer_config flexmatch --metrics "Test Accuracy" "Pool Accuracy" --strategies random confidence entropy margin coreset galaxy badge --smoothing max --colors 'tab:blue' 'tab:orange' 'tab:green' 'tab:red' 'tab:purple' 'tab:brown' 'tab:pink'
python plot_training.py --wandb_name $wandb_name --dataset cifar10 --batch_size 200 --embed_model clip_ViTB32 --classifier_model shallow --trainer_config flexmatch --metrics "Test Accuracy" "Pool Accuracy" --strategies random confidence entropy margin coreset galaxy badge --smoothing max --colors 'tab:blue' 'tab:orange' 'tab:green' 'tab:red' 'tab:purple' 'tab:brown' 'tab:pink'
python plot_training.py --wandb_name $wandb_name --dataset cifar10 --batch_size 1000 --embed_model none --classifier_model clip_ViTB32 --trainer_config flexmatch --metrics "Pool Accuracy" --strategies random confidence entropy margin coreset galaxy badge bait --smoothing max --colors 'tab:blue' 'tab:orange' 'tab:green' 'tab:red' 'tab:purple' 'tab:brown' 'tab:pink' 'tab:olive' --y_lim .965 1
python plot_training.py --wandb_name $wandb_name --dataset cifar10 --batch_size 1000 --embed_model none --classifier_model clip_ViTB32 --trainer_config flexmatch --metrics "Test Accuracy" --strategies random confidence entropy margin coreset galaxy badge bait --smoothing max --colors 'tab:blue' 'tab:orange' 'tab:green' 'tab:red' 'tab:purple' 'tab:brown' 'tab:pink' 'tab:olive' --y_lim .96 .982
python plot_training.py --wandb_name $wandb_name --dataset cifar10 --batch_size 1000 --embed_model clip_ViTB32 --classifier_model linear --trainer_config flexmatch --metrics "Test Accuracy" "Pool Accuracy" --strategies random confidence entropy margin coreset galaxy badge bait --smoothing max --colors 'tab:blue' 'tab:orange' 'tab:green' 'tab:red' 'tab:purple' 'tab:brown' 'tab:pink' 'tab:olive'
python plot_training.py --wandb_name $wandb_name --dataset cifar10 --batch_size 1000 --embed_model clip_ViTB32 --classifier_model shallow --trainer_config flexmatch --metrics "Test Accuracy" "Pool Accuracy" --strategies random confidence entropy margin coreset galaxy badge bait --smoothing max --colors 'tab:blue' 'tab:orange' 'tab:green' 'tab:red' 'tab:purple' 'tab:brown' 'tab:pink' 'tab:olive'
python fetch_wandb_training.py --wandb_name $wandb_name --dataset cifar10 --batch_size 1000 --embed_model none --classifier_model clip_ViTB32 --trainer_config passive
python plot_training.py --wandb_name $wandb_name --dataset cifar10 --batch_size 1000 --embed_model none --classifier_model clip_ViTB32 --trainer_config passive --metrics "Pool Accuracy" --strategies random confidence entropy margin coreset galaxy badge bait --smoothing max --colors 'tab:blue' 'tab:orange' 'tab:green' 'tab:red' 'tab:purple' 'tab:brown' 'tab:pink'
python plot_training.py --wandb_name $wandb_name --dataset cifar10 --batch_size 1000 --embed_model none --classifier_model clip_ViTB32 --trainer_config passive --metrics "Test Accuracy" --strategies random confidence entropy margin coreset galaxy badge bait --smoothing max --colors 'tab:blue' 'tab:orange' 'tab:green' 'tab:red' 'tab:purple' 'tab:brown' 'tab:pink'
python fetch_wandb_eval.py --wandb_name $wandb_name --dataset cifar10 --eval_batch_size 200 --classifier_model shallow --eval_classifier_model clip_ViTB32 --trainer_config flexmatch --eval_trainer_config flexmatch
python fetch_wandb_eval.py --wandb_name $wandb_name --dataset cifar10 --eval_batch_size 1000 --classifier_model shallow --eval_classifier_model clip_ViTB32 --trainer_config flexmatch --eval_trainer_config flexmatch
python plot_eval.py --wandb_name $wandb_name --dataset cifar10 --eval_batch_size 200 --classifier_model shallow --eval_classifier_model clip_ViTB32 --trainer_config flexmatch --eval_trainer_config flexmatch --metrics "Pool Accuracy" --strategies random confidence entropy margin coreset galaxy badge --smoothing max --colors 'tab:blue' 'tab:orange' 'tab:green' 'tab:red' 'tab:purple' 'tab:brown' 'tab:pink' --y_lim .965 1
python plot_eval.py --wandb_name $wandb_name --dataset cifar10 --eval_batch_size 200 --classifier_model shallow --eval_classifier_model clip_ViTB32 --trainer_config flexmatch --eval_trainer_config flexmatch --metrics "Test Accuracy" --strategies random confidence entropy margin coreset galaxy badge --smoothing max --colors 'tab:blue' 'tab:orange' 'tab:green' 'tab:red' 'tab:purple' 'tab:brown' 'tab:pink' --y_lim .96 .982
python plot_eval.py --wandb_name $wandb_name --dataset cifar10 --eval_batch_size 1000 --classifier_model shallow --eval_classifier_model clip_ViTB32 --trainer_config flexmatch --eval_trainer_config flexmatch --metrics "Pool Accuracy" --strategies random confidence entropy margin coreset galaxy badge bait --smoothing max --colors 'tab:blue' 'tab:orange' 'tab:green' 'tab:red' 'tab:purple' 'tab:brown' 'tab:pink' 'tab:olive' --y_lim .965 1
python plot_eval.py --wandb_name $wandb_name --dataset cifar10 --eval_batch_size 1000 --classifier_model shallow --eval_classifier_model clip_ViTB32 --trainer_config flexmatch --eval_trainer_config flexmatch --metrics "Test Accuracy" --strategies random confidence entropy margin coreset galaxy badge bait --smoothing max --colors 'tab:blue' 'tab:orange' 'tab:green' 'tab:red' 'tab:purple' 'tab:brown' 'tab:pink' 'tab:olive' --y_lim .96 .982
python fetch_wandb_training.py --wandb_name $wandb_name --dataset cifar100 --batch_size 1000 --embed_model none --classifier_model clip_ViTB32 --trainer_config flexmatch
python fetch_wandb_training.py --wandb_name $wandb_name --dataset cifar100 --batch_size 1000 --embed_model clip_ViTB32 --classifier_model linear --trainer_config flexmatch
python fetch_wandb_training.py --wandb_name $wandb_name --dataset cifar100 --batch_size 1000 --embed_model clip_ViTB32 --classifier_model shallow --trainer_config flexmatch
python plot_training.py --wandb_name $wandb_name --dataset cifar100 --batch_size 1000 --embed_model none --classifier_model clip_ViTB32 --trainer_config flexmatch --metrics "Test Accuracy" "Pool Accuracy" --strategies random confidence entropy margin coreset galaxy badge --smoothing max --colors 'tab:blue' 'tab:orange' 'tab:green' 'tab:red' 'tab:purple' 'tab:brown' 'tab:pink'
python plot_training.py --wandb_name $wandb_name --dataset cifar100 --batch_size 1000 --embed_model clip_ViTB32 --classifier_model linear --trainer_config flexmatch --metrics "Test Accuracy" "Pool Accuracy" --strategies random confidence entropy margin coreset galaxy badge --smoothing max --colors 'tab:blue' 'tab:orange' 'tab:green' 'tab:red' 'tab:purple' 'tab:brown' 'tab:pink'
python plot_training.py --wandb_name $wandb_name --dataset cifar100 --batch_size 1000 --embed_model clip_ViTB32 --classifier_model shallow --trainer_config flexmatch --metrics "Test Accuracy" "Pool Accuracy" --strategies random confidence entropy margin coreset galaxy badge --smoothing max --colors 'tab:blue' 'tab:orange' 'tab:green' 'tab:red' 'tab:purple' 'tab:brown' 'tab:pink'
python fetch_wandb_training.py --wandb_name $wandb_name --dataset fmow --batch_size 3000 --embed_model none --classifier_model clip_ViTB32 --trainer_config flexmatch
python fetch_wandb_training.py --wandb_name $wandb_name --dataset fmow --batch_size 3000 --embed_model clip_ViTB32 --classifier_model linear --trainer_config flexmatch
python fetch_wandb_training.py --wandb_name $wandb_name --dataset fmow --batch_size 3000 --embed_model clip_ViTB32 --classifier_model shallow --trainer_config flexmatch
python fetch_wandb_training.py --wandb_name $wandb_name --dataset iwildcam --batch_size 3000 --embed_model none --classifier_model clip_ViTB32 --trainer_config flexmatch
python fetch_wandb_training.py --wandb_name $wandb_name --dataset iwildcam --batch_size 3000 --embed_model clip_ViTB32 --classifier_model linear --trainer_config flexmatch
python fetch_wandb_training.py --wandb_name $wandb_name --dataset iwildcam --batch_size 3000 --embed_model clip_ViTB32 --classifier_model shallow --trainer_config flexmatch
python plot_training.py --wandb_name $wandb_name --dataset iwildcam --batch_size 3000 --embed_model none --classifier_model clip_ViTB32 --trainer_config flexmatch --metrics "Test Macro F1" "Pool Macro F1" --strategies random confidence entropy margin coreset galaxy badge --smoothing max --colors 'tab:blue' 'tab:orange' 'tab:green' 'tab:red' 'tab:purple' 'tab:brown' 'tab:pink'
python plot_training.py --wandb_name $wandb_name --dataset iwildcam --batch_size 3000 --embed_model clip_ViTB32 --classifier_model linear --trainer_config flexmatch --metrics "Test Macro F1" "Pool Macro F1" --strategies random confidence entropy margin coreset galaxy badge --smoothing max --colors 'tab:blue' 'tab:orange' 'tab:green' 'tab:red' 'tab:purple' 'tab:brown' 'tab:pink'
python plot_training.py --wandb_name $wandb_name --dataset iwildcam --batch_size 3000 --embed_model clip_ViTB32 --classifier_model shallow --trainer_config flexmatch --metrics "Test Macro F1" "Pool Macro F1" --strategies random confidence entropy margin coreset galaxy badge --smoothing max --colors 'tab:blue' 'tab:orange' 'tab:green' 'tab:red' 'tab:purple' 'tab:brown' 'tab:pink'
python plot_training.py --wandb_name $wandb_name --dataset fmow --batch_size 3000 --embed_model none --classifier_model clip_ViTB32 --trainer_config flexmatch --metrics "Test Accuracy" "Pool Accuracy" --strategies random confidence entropy margin coreset galaxy badge --smoothing max --colors 'tab:blue' 'tab:orange' 'tab:green' 'tab:red' 'tab:purple' 'tab:brown' 'tab:pink'
python plot_training.py --wandb_name $wandb_name --dataset fmow --batch_size 3000 --embed_model clip_ViTB32 --classifier_model linear --trainer_config flexmatch --metrics "Test Accuracy" "Pool Accuracy" --strategies random confidence entropy margin coreset galaxy badge --smoothing max --colors 'tab:blue' 'tab:orange' 'tab:green' 'tab:red' 'tab:purple' 'tab:brown' 'tab:pink'
python plot_training.py --wandb_name $wandb_name --dataset fmow --batch_size 3000 --embed_model clip_ViTB32 --classifier_model shallow --trainer_config flexmatch --metrics "Test Accuracy" "Pool Accuracy" --strategies random confidence entropy margin coreset galaxy badge --smoothing max --colors 'tab:blue' 'tab:orange' 'tab:green' 'tab:red' 'tab:purple' 'tab:brown' 'tab:pink'
python fetch_wandb_training.py --wandb_name $wandb_name --dataset imagenet --batch_size 50000 --embed_model none --classifier_model clip_ViTB32 --trainer_config flexmatch
python fetch_wandb_training.py --wandb_name $wandb_name --dataset imagenet --batch_size 50000 --embed_model none --classifier_model coca_ViTB32 --trainer_config flexmatch
python fetch_wandb_training.py --wandb_name $wandb_name --dataset imagenet --batch_size 50000 --embed_model clip_ViTB32 --classifier_model linear --trainer_config flexmatch
python fetch_wandb_training.py --wandb_name $wandb_name --dataset imagenet --batch_size 50000 --embed_model coca_ViTB32 --classifier_model linear --trainer_config flexmatch
python fetch_wandb_training.py --wandb_name $wandb_name --dataset imagenet --batch_size 50000 --embed_model clip_ViTB32 --classifier_model shallow --trainer_config flexmatch
python fetch_wandb_training.py --wandb_name $wandb_name --dataset imagenet --batch_size 50000 --embed_model coca_ViTB32 --classifier_model shallow --trainer_config flexmatch
python plot_training.py --wandb_name $wandb_name --dataset imagenet --batch_size 50000 --embed_model none --classifier_model clip_ViTB32 --trainer_config flexmatch --metrics "Test Accuracy" "Pool Accuracy" --strategies random confidence entropy margin coreset badge --smoothing max --colors 'tab:blue' 'tab:orange' 'tab:green' 'tab:red' 'tab:purple' 'tab:pink'
python plot_training.py --wandb_name $wandb_name --dataset imagenet --batch_size 50000 --embed_model clip_ViTB32 --classifier_model linear --trainer_config flexmatch --metrics "Test Accuracy" "Pool Accuracy" --strategies random confidence entropy margin coreset badge --smoothing max --colors 'tab:blue' 'tab:orange' 'tab:green' 'tab:red' 'tab:purple' 'tab:brown' 'tab:pink'
python plot_training.py --wandb_name $wandb_name --dataset imagenet --batch_size 50000 --embed_model clip_ViTB32 --classifier_model shallow --trainer_config flexmatch --metrics "Test Accuracy" "Pool Accuracy" --strategies random confidence entropy margin coreset badge --smoothing max --colors 'tab:blue' 'tab:orange' 'tab:green' 'tab:red' 'tab:purple' 'tab:brown' 'tab:pink'
python plot_training.py --wandb_name $wandb_name --dataset imagenet --batch_size 50000 --embed_model none --classifier_model coca_ViTB32 --trainer_config flexmatch --metrics "Test Accuracy" "Pool Accuracy" --strategies random confidence entropy margin coreset badge --smoothing max --colors 'tab:blue' 'tab:orange' 'tab:green' 'tab:red' 'tab:purple' 'tab:pink'
python plot_training.py --wandb_name $wandb_name --dataset imagenet --batch_size 50000 --embed_model coca_ViTB32 --classifier_model linear --trainer_config flexmatch --metrics "Test Accuracy" "Pool Accuracy" --strategies random confidence entropy margin coreset badge --smoothing max --colors 'tab:blue' 'tab:orange' 'tab:green' 'tab:red' 'tab:purple' 'tab:brown' 'tab:pink'
python plot_training.py --wandb_name $wandb_name --dataset imagenet --batch_size 50000 --embed_model coca_ViTB32 --classifier_model shallow --trainer_config flexmatch --metrics "Test Accuracy" "Pool Accuracy" --strategies random confidence entropy margin coreset badge --smoothing max --colors 'tab:blue' 'tab:orange' 'tab:green' 'tab:red' 'tab:purple' 'tab:brown' 'tab:pink'