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-experiment_group: Lines Experiments
-experiments:
- - train_args:
- batch_size: 64
- max_epochs: &max_epochs 64
- 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_025816/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
- train: true
- test: true
- test_metric: test_cer