The Mask R-CNN(-RS) and Cascade RCNN-RS models.

backbone tf.keras.Model, the backbone network.
decoder tf.keras.Model, the decoder network.
rpn_head the RPN head.
detection_head the detection head or a list of heads.
roi_generator the ROI generator.
roi_sampler a single ROI sampler or a list of ROI samplers for cascade detection heads.
roi_aligner the ROI aligner.
detection_generator the detection generator.
mask_head the mask head.
mask_sampler the mask sampler.
mask_roi_aligner the ROI alginer for mask prediction.
class_agnostic_bbox_pred if True, perform class agnostic bounding box prediction. Needs to be True for Cascade RCNN models.
cascade_class_ensemble if True, ensemble classification scores over all detection heads.
min_level Minimum level in output feature maps.
max_level Maximum level in output feature maps.
num_scales A number representing intermediate scales added on each level. For instances, num_scales=2 adds one additional intermediate anchor scales [2^0, 2^0.5] on each level.
aspect_ratios A list representing the aspect raito anchors added on each level. The number indicates the ratio of width to height. For instances, aspect_ratios=[1.0, 2.0, 0.5] adds three anchors on each scale level.
anchor_size A number representing the scale of size of the base anchor to the feature stride 2^level.
outer_boxes_scale a float to scale up the bounding boxes to generate more inclusive masks. The scale is expected to be >=1.0.
**kwargs keyword arguments to be passed.

checkpoint_items Returns a dictionary of items to be additionally checkpointed.



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Calls the model on new inputs and returns the outputs as tensors.

In this case call() just reapplies all ops in the graph to the new inputs (e.g. build a new computational graph from the provided inputs).

inputs Input tensor, or dict/list/tuple of input tensors.
training Boolean or boolean scalar tensor, indicating whether to run the Network in training mode or inference mode.
mask A mask or list of masks. A mask can be either a boolean tensor or None (no mask). For more details, check the guide here.

A tensor if there is a single output, or a list of tensors if there are more than one outputs.