 Here is a simplified example of how you might structure this using the PyTorch Vision Transforms library and the MMDet toolbox for box augmentations. Please note that you'll need to install these libraries and adjust the code to fit your specific needs.

```python
import torch
import torchvision.transforms as transforms
from mmdet.datasets.pipelines import Compose, BoxTypeFormatter
from mmdet.datasets.transforms.augmentation import RandomFlip, Scale, RandomCrop, RandomSaturation, RandomHue, RandomBrightness, RandomContrast

# Define image augmentation pipeline
image_aug = Compose([
    RandomContrast(0.15, 1.0),
    RandomBrightness(0.1),
    RandomSaturation(0.1),
    RandomHue(0.1),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])

# Define box augmentation pipeline
box_aug = Compose([
    BoxTypeFormatter(),
    Scale(min_size=832),
    RandomCrop(size=832),
    RandomFlip(),
])

# Define preprocessing pipeline for boxes
preprocess_boxes = Compose([
    lambda boxes: torch.tensor(boxes, dtype=torch.float32),
    lambda boxes: torchvision.ops.box_convert(boxes, 'xywh', 'xyxy'),
    lambda boxes: torch.clamp(boxes, min=0, max=1),
    lambda boxes: torch.stack((boxes[:, :2] - boxes[:, 2:]) / boxes[:, 2:], dim=1),
    lambda boxes: torch.cat((boxes, torch.zeros_like(boxes[:, :1])), dim=1),
    lambda boxes: torch.cat((boxes, torch.zeros((boxes.shape[0], 4))), dim=1),
    lambda boxes: torch.cat((boxes, torch.zeros((boxes.shape[0], 10))), dim=1),
    lambda boxes: torch.nn.functional.one_hot(boxes[:, 4].long(), num_classes=10)
])

# Define a pipeline for drawing boxes
def draw_boxes(img, boxes):
    # Implement your box drawing logic here
    pass

# Define a sequence generator for processing batches of data
def collate_fn(batch):
    img, boxes = zip(*batch)
    img = torch.stack(img, dim=0)
    boxes = torch.cat(boxes, dim=0)
    return img, boxes

# Download an image and perform augmentations
# Assuming download_image function downloads an image and returns it as a PIL Image
img = download_image('url')
img = image_aug(img)
boxes = [(0.1, 0.2, 0.4, 0.5), (0.6, 0.7, 0.8, 0.9)]  # example boxes
boxes = box_aug(boxes)
boxes = preprocess_boxes(boxes)

# Draw boxes on the augmented image
img_with_boxes = draw_boxes(img, boxes)
```

This code does not include the implementation of the `download_image` function, the box drawing logic, and the `collate_fn` function for batch processing. You'll need to implement these parts according to your specific requirements. Also, you might need to adjust the code to fit your specific use case, such as using different libraries for image downloading or box drawing, or changing the augmentation parameters.