code: from config import Config
  import numpy as np
  class BowlConfig(Config):
      """Configuration for training on the toy shapes dataset.
      Derives from the base Config class and overrides values specific
      to the toy shapes dataset.
      """
      # Give the configuration a recognizable name
      NAME = "bowl"
  
      # Train on 1 GPU and 8 images per GPU. We can put multiple images on each
      # GPU because the images are small. Batch size is 8 (GPUs * images/GPU).
      GPU_COUNT = 1
      IMAGES_PER_GPU = 1
      LEARNING_RATE = 0.00005
      # Number of classes (including background)
      NUM_CLASSES = 1 + 1 # background + nuclei
  
      # Use small images for faster training. Set the limits of the small side
      # the large side, and that determines the image shape.
      IMAGE_MIN_DIM = 256
      IMAGE_MAX_DIM = 256
  
      # Use smaller anchors because our image and objects are small
      RPN_ANCHOR_SCALES = (8, 16, 32, 64)  # anchor side in pixels
  
      # Reduce training ROIs per image because the images are small and have
      # few objects. Aim to allow ROI sampling to pick 33% positive ROIs.
      TRAIN_ROIS_PER_IMAGE = 500
  
      STEPS_PER_EPOCH = 600
  
      # use small validation steps since the epoch is small
      VALIDATION_STEPS = 10
      MEAN_PIXEL = np.array([0.0, 0.0, 0.0])
      USE_MINI_MASK = False
      MINI_MASK_SHAPE = (28, 28)	
      MAX_GT_INSTANCES = 500
  
      DETECTION_MAX_INSTANCES = 512
  
      RESNET_ARCHITECTURE = "resnet50"
  
  
  bowl_config = BowlConfig()
  bowl_config.display()
  
apis: numpy.array