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experiment_group: Lines Experiments
experiments:
- train_args:
batch_size: 64
max_epochs: &max_epochs 32
dataset:
type: IamLinesDataset
args:
subsample_fraction: null
transform: null
target_transform: null
train_args:
num_workers: 8
train_fraction: 0.85
model: LineCTCModel
metrics: [cer, wer]
network:
type: LineRecurrentNetwork
args:
# backbone: ResidualNetwork
# backbone_args:
# in_channels: 1
# num_classes: 64 # Embedding
# depths: [2,2]
# block_sizes: [32, 64]
# activation: selu
# stn: false
backbone: ResidualNetwork
backbone_args:
pretrained: training/experiments/CharacterModel_EmnistDataset_ResidualNetwork/0920_010806/model/best.pt
freeze: false
flatten: false
input_size: 64
hidden_size: 64
bidirectional: true
num_layers: 2
num_classes: 80
patch_size: [28, 18]
stride: [1, 4]
criterion:
type: CTCLoss
args:
blank: 79
optimizer:
type: AdamW
args:
lr: 1.e-02
betas: [0.9, 0.999]
eps: 1.e-08
weight_decay: 5.e-4
amsgrad: false
lr_scheduler:
type: OneCycleLR
args:
max_lr: 1.e-02
epochs: *max_epochs
anneal_strategy: cos
pct_start: 0.475
cycle_momentum: true
base_momentum: 0.85
max_momentum: 0.9
div_factor: 10
final_div_factor: 10000
interval: step
# lr_scheduler:
# type: CosineAnnealingLR
# args:
# T_max: *max_epochs
swa_args:
start: 48
lr: 5.e-2
callbacks: [Checkpoint, ProgressBar, WandbCallback, WandbImageLogger, EarlyStopping]
callback_args:
Checkpoint:
monitor: val_loss
mode: min
ProgressBar:
epochs: *max_epochs
EarlyStopping:
monitor: val_loss
min_delta: 0.0
patience: 10
mode: min
WandbCallback:
log_batch_frequency: 10
WandbImageLogger:
num_examples: 6
verbosity: 1 # 0, 1, 2
resume_experiment: null
test: true
test_metric: test_cer
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