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