#!/bin/bash # # Parametrized Training # 100 epochs, frozen_processor: http://deplo-mlflo-1ssxo94f973sj-890390d809901dbf.elb.eu-central-1.amazonaws.com/#/experiments/49/runs/2803f44514e34a0f87d591520706e876 # model_uri="s3://mlflow-artifacts-601883093460/49/2803f44514e34a0f87d591520706e876/artifacts/model" # used for training current model to 100% train and 80% val accuracy # python train.py \ # --experiment_name parametrized \ # --classifier_uri "${model_uri}" \ # --run_name par_full_kurt \ # --dataset Microscopy \ # --lr 1e-5 \ # --epochs 50 \ # --freeze_classifier \ # --freeze_processor \ # # Adversarial Training # python train.py \ # --experiment_name adversarial \ # --run_name adv_frozen_processor \ # --classifier_uri "${model_uri}" \ # --dataset Microscopy \ # --adv_training \ # --lr 1e-3 \ # --epochs 7 \ # --freeze_classifier \ # --track_processing \ # --track_every_epoch \ # --log_model=False \ # --adv_aux_weight=0.1 \ # --adv_aux_loss "l2" \ # --adv_aux_weight=2e-5 \ # --adv_aux_weight=2e-5 \ # --adv_aux_weight=1.9e-5 \ # Cross pipeline training (Segmentation/Classification) # Static Pipeline Script # datasets="Microscopy Drone DroneSegmentation" datasets="DroneSegmentation" augmentations="weak strong none" demosaicings="bilinear malvar2004 menon2007" sharpenings="sharpening_filter unsharp_masking" denoisings="median_denoising gaussian_denoising" for augment in $augmentations do for data in $datasets do for demosaicing in $demosaicings do for sharpening in $sharpenings do for denoising in $denoisings do python train.py \ --experiment_name ABtesting \ --run_name "$data"_"$demosaicing"_"$sharpening"_"$denoising"_"$augment" \ --dataset "$data" \ --batch_size 4 \ --lr 1e-5 \ --epochs 100 \ --sp_debayer "$demosaicing" \ --sp_sharpening "$sharpening" \ --sp_denoising "$denoising" \ --processing_mode "static" \ --augmentation "$augment" \ --n_split 5 \ done done done done done