class_docstr: Keras layer for filtering detections using score threshold and NMS.
      
class_docstr_tok: ["Keras","layer","for","filtering","detections","using","score","threshold","and","NMS","."]
docstr: Filters detections using score threshold, NMS and selecting the top-k detections.
  
  Args
      nms                   : Flag to enable/disable NMS.
      class_specific_filter : Whether to perform filtering per class, or take the best scoring class and filter those.
      nms_threshold         : Threshold for the IoU value to determine when a box should be suppressed.
      score_threshold       : Threshold used to prefilter the boxes with.
      max_detections        : Maximum number of detections to keep.
      parallel_iterations   : Number of batch items to process in parallel.
docstr_tok: ["Filters","detections","using","score","threshold",",","NMS","and","selecting","the","top-k","detections",".","Args","nms",":","Flag","to","enable\/disable","NMS",".","class_specific_filter",":","Whether","to","perform","filtering","per","class",",","or","take","the","best","scoring","class","and","filter","those",".","nms_threshold",":","Threshold","for","the","IoU","value","to","determine","when","a","box","should","be","suppressed",".","score_threshold",":","Threshold","used","to","prefilter","the","boxes","with",".","max_detections",":","Maximum","number","of","detections","to","keep",".","parallel_iterations",":","Number","of","batch","items","to","process","in","parallel","."]
code: def __init__(
      self,
      nms=True,
      class_specific_filter=True,
      nms_threshold=0.5,
      score_threshold=0.05,
      max_detections=300,
      parallel_iterations=32,
      **kwargs,
  ):
      """Filters detections using score threshold, NMS and selecting the top-k detections.
  
      Args
          nms                   : Flag to enable/disable NMS.
          class_specific_filter : Whether to perform filtering per class, or take the best scoring class and filter those.
          nms_threshold         : Threshold for the IoU value to determine when a box should be suppressed.
          score_threshold       : Threshold used to prefilter the boxes with.
          max_detections        : Maximum number of detections to keep.
          parallel_iterations   : Number of batch items to process in parallel.
      """
      self.nms = nms
      self.class_specific_filter = class_specific_filter
      self.nms_threshold = nms_threshold
      self.score_threshold = score_threshold
      self.max_detections = max_detections
      self.parallel_iterations = parallel_iterations
      super(FilterDetections, self).__init__(**kwargs)
  
file_path: ImageAI/imageai_tf_deprecated/Detection/keras_retinanet/layers/filter_detections.py
imports: import tensorflow
  from tensorflow import keras
  from ..None import backend
label_desc: Use image node_id 1 to create a new FilterDetections object from inherited base classes: keras.layers with example: obj = FilterDetections(nms, class_specific_filter, nms_threshold, score_threshold, max_detections, parallel_iterations)