  ```
import tensorflow as tf
from paz.abstract import Processor
from paz.processors import PreprocessBoxes, PreprocessImage, AugmentImage, AugmentBoxes, DrawBoxData2D, ShowBoxes, AugmentDetection

class PreprocessBoxes(Processor):
    def __init__(self, match_threshold=0.5, encode_boxes=True, one_hot_box_class=True):
        self.match_threshold = match_threshold
        self.encode_boxes = encode_boxes
        self.one_hot_box_class = one_hot_box_class

    def process(self, boxes, image):
        # Preprocess boxes by matching them, encoding them, and converting the box class to a one-hot vector
        pass

class PreprocessImage(Processor):
    def __init__(self, resize_shape=(256, 256), subtract_mean=True, normalize=True):
        self.resize_shape = resize_shape
        self.subtract_mean = subtract_mean
        self.normalize = normalize

    def process(self, image):
        # Preprocess an RGB image by resizing it and either subtracting a mean or normalizing it
        pass

class AugmentImage(Processor):
    def __init__(self, resize_shape=(256, 256), blend_background=True, random_contrast=True, random_brightness=True, random_saturation=True, random_hue=True):
        self.resize_shape = resize_shape
        self.blend_background = blend_background
        self.random_contrast = random_contrast
        self.random_brightness = random_brightness
        self.random_saturation = random_saturation
        self.random_hue = random_hue

    def process(self, image):
        # Augment an RGB image by resizing it, blending it with a random cropped background, and applying random contrast, brightness, saturation, and hue adjustments
        pass

class AugmentBoxes(Processor):
    def __init__(self, expand_boxes=True, random_sample_crop=True, random_flip=True):
        self.expand_boxes = expand_boxes
        self.random_sample_crop = random_sample_crop
        self.random_flip = random_flip

    def process(self, boxes, image):
        # Augment bounding boxes by converting them to image box coordinates, expanding them, applying random sample cropping and random flipping
        pass

class DrawBoxData2D(Processor):
    def __init__(self, color=(0, 255, 0), thickness=2):
        self.color = color
        self.thickness = thickness

    def process(self, boxes, image):
        # Draw 2D box data on an image
        pass

class ShowBoxes(Processor):
    def __init__(self, decode_boxes=True, denormalize_boxes=True, draw_boxes=True):
        self.decode_boxes = decode_boxes
        self.denormalize_boxes = denormalize_boxes
        self.draw_boxes = draw_boxes

    def process(self, boxes, image):
        # Show boxes on an image after resizing the image, decoding the boxes, denormalizing them, and drawing them on the image
        pass

class AugmentDetection(Processor):
    def __init__(self, model, data, train_mode=True):
        self.model = model
        self.data = data
        self.train_mode = train_mode

    def process(self, sample):
        # Augment boxes and images for object detection by loading an image, applying image and box augmentation if in training mode, preprocessing the image and boxes, and wrapping the sequence
        pass

if __name__ == '__main__':
    # Set up GPU memory growth
    gpus = tf.config.experimental.list_physical_devices('GPU')
    if gpus:
        tf.config.experimental.set_memory_growth(gpus[0], True)

    # Load model and data
    model = tf.keras.models.load_model('model.h5')
    data = tf.data.Dataset.from_tensor_slices(('path/to/images', 'path/to/boxes'))

    # Apply augmentation processors to each sample in the dataset
    data = data.map(AugmentDetection(model, data, train_mode=True))
    data = data.map(ShowBoxes())
```