train.ini.example

[dataset]
; Data directory that contains class specific image directories.
; NOTE! Only .png images are currently supported.
path = /path/to/data/train
; How to split the images between train, validation and test.
; Test is optional when using `external_test`
split = 0.9, 0.1
; Additional dataset to test the model with after training.
external_test = /path/to/data/test
; Minimun allowed class size (number of images).
min_N =
; Maximum allowed class size, useful for test runs.
max_N =
; Classes to exclude from training.
; Example: Unclassified, Beads
exclude =
; Random seed to use when shuffling and splitting data.
random_seed = 42
; Oversamples training images until this class size is reached.
oversample_until = 100
; Oversamples training images with this exponential decay.
; Example: 0.995
oversample_with_decay =

[model]
; Directory in which to save trained models.
path = /path/to/models
; Name of pre-trained network architecture available in PyTorch Hub.
; More info here: https://pytorch.org/vision/stable/models.html
; NOTE! Only some of the models have been tested, and only ResNets have been actually used.
network = resnet18
; Pre-trained weights to use, see above link for all options.
; DEFAULT = best available, leave empty for no pre-trained weights.
weights = DEFAULT
; Version number attached to end of network name.
; auto = automatically incrementing number, e.g., resnet18_1, resnet18_2, ...
id = auto
; Allow overwriting saved models, won't happen when `id = auto`.
exist_ok = no
; New head (i.e., last) layers inserted on top of pre-trained network.
; The final classification layer is automatically appended to this
; based on the number of classes.
head = 256, 128
; Optional dropout(s) to insert between head layers.
; Format: index, probability; index, probability; ...
; Example: -2, 0.5
; This would insert Dropout(0.5) before second to last head layer.
dropout =

[image]
; Resize images to this size. First value is number of color channels.
shape = 3, 180, 180
; Transformations to apply (for every image).
; Options: flip, translate, zoom, rotate, brightness
; NOTE! Order matters (brightness should always be last)
augmentations = flip, translate, zoom, brightness
imagenet_normalization = no
; Options: mode, black, white
border = mode
zoom_range = 0.6, 1.4
brightness_range = 0.95, 1.1
max_rotation = 10
batch_size = 256
; Parameter for PyTorch's DataLoader.
num_workers = 8

[train]
gpu = yes
max_epochs = 100
; Training is stopped if validation loss doesn't decrease for this many epochs.
early_stop_patience = 12
; Initial learning rate. Both `lr_warmup` and `lr_reduction` will decrease it.
learning_rate = 0.01
optimizer = Adam

[lr_warmup]
; Unlock trainable layers and decrease learning rates incrementally.
use = yes
factor_1 = 0.1
factor_2 = 0.5
step_1 = 4
step_2 = 14
step_3 = 24
verbose = yes

[lr_reduction]
; Decrease learning rate when loss stops decreasing.
use = yes
factor = 0.1
; When using `lr_warmup`, patience starts counting only after its `step_3`.
patience = 4
verbose = yes