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config_VOCSegmentation.yml
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config_VOCSegmentation.yml
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# parameters configuration
mode: train
root: data/
predefined_dataset: VOCSegmentation
classes:
[
"background",
"aeroplane",
"bicycle",
"bird",
"boat",
"bottle",
"bus",
"car",
"cat",
"chair",
"cow",
"diningtable",
"dog",
"horse",
"motorbike",
"person",
"pottedplant",
"sheep",
"sofa",
"train",
"tvmonitor",
]
max_samples: null
batch_size: 32
num_workers: 0
device: cuda
lr: 1e-3
model_name: tf_mobilenetv3_small_minimal_100
in_chans: 3
loss_function_name: BCEWithLogitsLoss
checkpoint_path: null
seed: 0
early_stopping: True
patience: 3
default_root_dir: save/
gpus: -1
precision: 32
max_epochs: 100
color_space: RGB # (3x8-bit pixels, true color)
web_interface: True
examples:
[
"examples/VOCSegmentation/image_000032.jpg",
"examples/VOCSegmentation/image_000033.jpg",
"examples/VOCSegmentation/image_000039.jpg",
"examples/VOCSegmentation/image_000042.jpg",
"examples/VOCSegmentation/image_000061.jpg",
]
tuning_test: False
cpu_resources_per_trial: 1
gpu_resources_per_trial: 1
num_samples: 100
# transforms configuration
transforms_config:
train:
Resize:
- 256
- 256
RandomVerticalFlip:
RandomHorizontalFlip:
RandomRotation: 90
ToTensor:
RandomErasing:
val:
Resize:
- 256
- 256
ToTensor:
test:
Resize:
- 256
- 256
ToTensor:
predict:
Resize:
- 256
- 256
ToTensor:
# target transforms configuration
target_transforms_config:
train:
OneHotEncoder:
num_classes: null
val:
OneHotEncoder:
num_classes: null
test:
OneHotEncoder:
num_classes: null
predict:
OneHotEncoder:
num_classes: null
# optimizers configuration
optimizers_config:
Adam:
betas:
- 0.9
- 0.999
eps: 1e-08
weight_decay: 0
amsgrad: False
# learning rate schedulers configuration
lr_schedulers_config:
CosineAnnealingLR:
T_max: 10
# hyperparameter space configuration
hyperparameter_space_config:
lr:
uniform:
lower: 1e-4
upper: 1e-1
max_epochs:
randint:
lower: 10
upper: 200