Creates a detection head.
tfm.vision.heads.DetectionHead(
num_classes: int,
num_convs: int = 0,
num_filters: int = 256,
use_separable_conv: bool = False,
num_fcs: int = 2,
fc_dims: int = 1024,
class_agnostic_bbox_pred: bool = False,
activation: str = 'relu',
use_sync_bn: bool = False,
norm_momentum: float = 0.99,
norm_epsilon: float = 0.001,
kernel_regularizer: Optional[tf.keras.regularizers.Regularizer] = None,
bias_regularizer: Optional[tf.keras.regularizers.Regularizer] = None,
**kwargs
)
Args |
num_classes
|
An int for the number of classes.
|
num_convs
|
An int number that represents the number of the intermediate
convolution layers before the FC layers.
|
num_filters
|
An int number that represents the number of filters of the
intermediate convolution layers.
|
use_separable_conv
|
A bool that indicates whether the separable
convolution layers is used.
|
num_fcs
|
An int number that represents the number of FC layers before
the predictions.
|
fc_dims
|
An int number that represents the number of dimension of the FC
layers.
|
class_agnostic_bbox_pred
|
bool , indicating whether bboxes should be
predicted for every class or not.
|
activation
|
A str that indicates which activation is used, e.g. 'relu',
'swish', etc.
|
use_sync_bn
|
A bool that indicates whether to use synchronized batch
normalization across different replicas.
|
norm_momentum
|
A float of normalization momentum for the moving average.
|
norm_epsilon
|
A float added to variance to avoid dividing by zero.
|
kernel_regularizer
|
A tf.keras.regularizers.Regularizer object for
Conv2D. Default is None.
|
bias_regularizer
|
A tf.keras.regularizers.Regularizer object for Conv2D.
|
**kwargs
|
Additional keyword arguments to be passed.
|
Methods
call
View source
call(
inputs: tf.Tensor, training: bool = None
)
Forward pass of box and class branches for the Mask-RCNN model.
Args |
inputs
|
A tf.Tensor of the shape [batch_size, num_instances, roi_height,
roi_width, roi_channels], representing the ROI features.
|
training
|
a bool indicating whether it is in training mode.
|
Returns |
class_outputs
|
A tf.Tensor of the shape
[batch_size, num_rois, num_classes], representing the class predictions.
|
box_outputs
|
A tf.Tensor of the shape
[batch_size, num_rois, num_classes * 4], representing the box
predictions.
|