add pdf and ipynb to git lfs

improve plotting of heatmaps and results

fix error in resnet code, don*t run constructor in place

add image stiching to exploration notebook

add notebook to be able to evaluate more models

add better contour plot using argmin and abs

add contour images to notebook

add drawing heatmap of example focus stack

add gpu support to notebook

add example images to notebook

rerun notebook with latest model

rerun and add logs for focusResNet101_pretrained_150

rerun and add logs for focusResNet_150

change to pretrained resnet50

rerun and add logs for focusMSE_150

rerun and add logs for focusMAE_150

rerun and add logs for focusConvMSE_besthyp_150

rerun and add logs for focusConvMSE_150

remove focus 75 MAE experiment

add .git to dockerignore

remove old runs to rerun all experiments

modify Dockerfiles to include git

readd cod modifications

add mlflow ass logger

add training opimization results for simple cnn

add logger that still contains mlflow

remove mlflow from many logger as it gives error for hyperparameter search

rename configuration file for experiment

reduce search space for hyperparameter optimisation

add mlflow as logger

remove log as mounted volume

add hyperparameter search for convolutional model

add results of simple cnn benchmark

Add simple fully convolutional network

add hyperparameter optimisation run

rename hyperparemeter search as it seems to not work with MSE

add runs for first experiments

change hyperparmeter search to use MSE instead of MAE

rename experiments for focus 75

rename focus MAE experiment

merge compose files

add user to docker-compose

update license to properly have original author

reduce search space for optuna experiments

remove all runs due to results being not useful (error in loss function)

change logger to also use csv

remove all checkpoint files from earlier runs

ignore .chpt files

added in_memory loading to reduce disk reads and increase speed

Add code so loss function uses torch.Size([x,1]) instead of torch.Size([x])