class ImageClassificationModel(hyperparams.Config):
num_classes: int = 0
input_size: List[int] = dataclasses.field(default_factory=list)
backbone: backbones.Backbone = backbones.Backbone(
type='darknet', darknet=backbones.Darknet())
dropout_rate: float = 0.0
norm_activation: common.NormActivation = common.NormActivation()
# Adds a Batch Normalization layer pre-GlobalAveragePooling in classification.
add_head_batch_norm: bool = False
class ImageClassificationModel(hyperparams.Config):
"""Image classification model config."""
num_classes: int = 0
input_size: List[int] = dataclasses.field(default_factory=lambda: [224, 224])
backbone: backbones.Backbone = backbones.Backbone(
type='darknet', darknet=backbones.Darknet())
dropout_rate: float = 0.0
norm_activation: common.NormActivation = common.NormActivation()
# Adds a Batch Normalization layer pre-GlobalAveragePooling in classification.
add_head_batch_norm: bool = False
kernel_initializer: str = 'VarianceScaling'
class Yolo(hyperparams.Config):
input_size: Optional[List[int]] = dataclasses.field(
default_factory=lambda: [512, 512, 3])
backbone: backbones.Backbone = backbones.Backbone(
type='darknet', darknet=backbones.Darknet(model_id='cspdarknet53'))
decoder: decoders.Decoder = decoders.Decoder(
type='yolo_decoder',
yolo_decoder=decoders.YoloDecoder(version='v4', type='regular'))
head: YoloHead = YoloHead()
detection_generator: YoloDetectionGenerator = YoloDetectionGenerator()
loss: YoloLoss = YoloLoss()
norm_activation: common.NormActivation = common.NormActivation(
activation='mish',
use_sync_bn=True,
norm_momentum=0.99,
norm_epsilon=0.001)
num_classes: int = 80
anchor_boxes: AnchorBoxes = AnchorBoxes()
darknet_based_model: bool = False
