 Here's the Python code based on your instructions. I'm assuming you have the `pandas`, `numpy`, `PIL`, and `torch` libraries installed. I'm also assuming that the `paz` library is named `paz` and the classes and functions within it are as follows: `Box`, `Image`, `Boxes`, `Images`, `augment_image`, `augment_boxes`, `draw_box_data_2d`, `resize_image`, `denormalize_boxes`, `decode_boxes`, `subtract_mean`, `normalize`, `random_crop`, `random_flip`, `random_contrast`, `random_brightness`, `random_saturation`, `random_hue`.

```python
import torch
import pandas as pd
import numpy as np
from PIL import Image
from paz.box import Box
from paz.image import Image as PazImage
from paz.boxes import Boxes
from paz.images import Images
from paz.utils import resize_image, denormalize_boxes, decode_boxes, subtract_mean, normalize, random_crop, random_flip, random_contrast, random_brightness, random_saturation, random_hue

class PreprocessBoxes:
    def __init__(self, match_threshold=0.5, box_encoder=None):
        self.match_threshold = match_threshold
        self.box_encoder = box_encoder or lambda boxes: boxes

    def __call__(self, boxes):
        encoded_boxes = self.box_encoder(boxes)
        matched_boxes = pd.DataFrame(encoded_boxes).groupby(encoded_boxes.index).apply(lambda group: group.iloc[0]).reset_index(drop=True)
        return matched_boxes

class PreprocessImage:
    def __init__(self, size=(640, 640), mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)):
        self.size = size
        self.mean = mean
        self.std = std

    def __call__(self, image):
        image = resize_image(image, self.size)
        image = image.convert('RGB')
        image = image.resize(self.size, Image.ANTIALIAS)
        image = np.array(image)
        image = subtract_mean(image, self.mean)
        image = normalize(image, self.std)
        return image

class AugmentImage:
    def __call__(self, image):
        image = resize_image(image, (256, 256))
        background = random_crop(image, (224, 224))
        image = image.resize((224, 224), Image.ANTIALIAS)
        image = np.array(image)
        image = np.stack([random_contrast(image[0], 0.8, 1.2),
                           random_brightness(image[1], 0.8, 1.2),
                           random_saturation(image[2], 0.8, 1.2),
                           random_hue(image[2], 0.1, 0.3)], axis=1)
        image = np.clip(image, 0, 255).astype(np.uint8)
        image = Image.fromarray(image)
        image.paste(background, (int((224 - 256) / 2), int((224 - 256) / 2)))
        return image

class AugmentBoxes:
    def __call__(self, boxes):
        boxes = boxes.apply(lambda box: Box(box.x1, box.y1, box.x2, box.y2, box.class_id), axis=1)
        boxes = boxes.apply(lambda box: box.to_image_box_coordinates(), axis=1)
        boxes = boxes.apply(lambda box: box.expand(0.2), axis=1)
        boxes = boxes.apply(lambda box: random_crop(box, (224, 224)), axis=1)
        boxes = boxes.apply(lambda box: random_flip(box), axis=1)
        return boxes

class DrawBoxData2D:
    def __call__(self, image, boxes):
        for box in boxes:
            draw_box_data_2d(image, box)

class ShowBoxes:
    def __init__(self, size=(640, 640)):
        self.size = size

    def __call__(self, image, boxes):
        image = resize_image(image, self.size)
        boxes = denormalize_boxes(boxes, self.size)
        boxes = decode_boxes(boxes, self.size)
        DrawBoxData2D()(image, boxes)
        return image

class AugmentDetection:
    def __init__(self, preprocess_image, augment_image, augment_boxes, preprocess_boxes, draw_box_data_2d, show_boxes):
        self.preprocess_image = preprocess_image
        self.augment_image = augment_image
        self.augment_boxes = augment_boxes
        self.preprocess_boxes = preprocess_boxes
        self.draw_box_data_2d = draw_box_data_2d
        self.show_boxes = show_boxes

    def __call__(self, image, boxes):
        if torch.rand(1).item() < 0.5:
            image = self.preprocess_image(image)
            boxes = self.preprocess_boxes(boxes)
        else:
            image = self.augment_image(image)
            boxes = self.augment_boxes(boxes)
        return image, boxes

# Main section
def main():
    # Set up GPU memory growth
    torch.backends.cudnn.enabled = True
    torch.backends.cudnn.benchmark = True
    torch.set_num_threads(4)

    # Load model and data
    model = ...
    dataset = ...

    # Create data loaders
    train_loader = torch.utils.data.DataLoader(dataset, batch_size=16, shuffle=True, num_workers=4)

    # Create processors
    preprocess_image = PreprocessImage()
    augment_image = AugmentImage()
    augment_boxes = AugmentBoxes()
    preprocess_boxes = PreprocessBoxes()
    draw_box_data_2d = DrawBoxData2D()
    show_boxes = ShowBoxes()
    augment_detection = AugmentDetection(preprocess_image, augment_image, augment_boxes, preprocess_boxes, draw_box_data_2d, show_boxes)

    # Apply processors to each sample in the dataset
    for images, boxes in train_loader:
        images = [preprocess_image(image) for image in images]
        boxes = preprocess_boxes(boxes)
        images, boxes = zip(*[augment_detection(image, box) for image, box in zip(images, boxes)])
        images = [show_boxes(image, boxes) for image in images]
        # Do something with the augmented images and boxes
```