Custom detection dataset in PyTorch - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the PyTorch Dataset class.

PyTorch

from torch.utils.data import [1]

Drag options to blanks, or click blank then click option'

ADataLoader

BTensor

CDataset

DModule

Attempts:

3 left

💡 Hint

Common Mistakes

Importing DataLoader instead of Dataset

Importing Tensor or Module which are unrelated here

We import Dataset to create a custom dataset class in PyTorch.

2fill in blank

medium

Complete the code to initialize the dataset with image paths and annotations.

PyTorch

class CustomDataset(Dataset):
    def __init__(self, [1], annotations):
        self.images = images
        self.annotations = annotations

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Adata

Bself

Clabels

Dimages

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'self' as a parameter name

Using 'labels' or 'data' which are not the parameter names here

The constructor takes images and annotations as inputs.

3fill in blank

hard

Fix the error in the __len__ method to return dataset size.

PyTorch

def __len__(self):
    return [1](self.images)

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Alen

Bsize

Ccount

Dlength

Attempts:

3 left

💡 Hint

Common Mistakes

Using count() which is not a built-in function for lists

Using size() or length() which are not valid Python functions

The len() function returns the number of items in a list.

4fill in blank

hard

Fill both blanks to load an image and its annotation in __getitem__.

PyTorch

def __getitem__(self, idx):
    image = Image.open(self.images[[1]])
    annotation = self.annotations[[2]]
    return image, annotation

Drag options to blanks, or click blank then click option'

Aidx

B0

C1

Dself

Attempts:

3 left

💡 Hint

Common Mistakes

Using 0 or 1 instead of idx, which always returns the first or second item

Using 'self' which is invalid as an index

We use the index idx to get the image path and annotation at the same position.

5fill in blank

hard

Fill all three blanks to apply a transform to the image if provided.

PyTorch

def __getitem__(self, idx):
    image = Image.open(self.images[idx])
    annotation = self.annotations[idx]
    if [1] is not None:
        image = [2](image)
    return [3], annotation

Drag options to blanks, or click blank then click option'

Aself.transform

Btransform

Cimage

Dself

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'transform' without self, which is undefined

Returning the original image variable name incorrectly

We check if self.transform exists, then apply it to the image, and return the transformed image.

Practice

1. What is the main purpose of creating a Custom detection dataset in PyTorch?

easy

A. To perform image classification without labels

B. To convert images to grayscale only

C. To generate random images for testing

D. To load images and their bounding boxes with labels for training detection models

Solution

1. Step 1: Understand dataset role in detection

   A detection dataset provides images along with bounding boxes and labels for each object to train detection models.

2. Step 2: Identify correct purpose

   Classification without labels, random image generation, and grayscale conversion do not provide bounding boxes or labels needed for detection tasks.

3. Final Answer:

   To load images and their bounding boxes with labels for training detection models -> Option D

4. Quick Check:

   Detection dataset = images + bounding boxes + labels [OK]

Hint: Detection datasets always include bounding boxes and labels [OK]

Common Mistakes:

• Thinking detection datasets only have images
• Confusing detection with classification datasets
• Ignoring bounding boxes in dataset

2. Which of the following is the correct way to define the __getitem__ method in a custom detection dataset class?

easy

A. def __getitem__(self): return image, label

B. def __getitem__(self, idx): return image, label

C. def __getitem__(self, idx): return image, bounding_boxes, labels

D. def getitem(self, idx): return image, bounding_boxes

Solution

1. Step 1: Recall __getitem__ signature

   The method must accept an index parameter and return image, bounding boxes, and labels for detection.

2. Step 2: Check options for correct syntax and return values

   def __getitem__(self, idx): return image, bounding_boxes, labels correctly uses def __getitem__(self, idx): and returns image, bounding boxes, and labels. Others miss parameters or return incomplete data.

3. Final Answer:

   def __getitem__(self, idx): return image, bounding_boxes, labels -> Option C

4. Quick Check:

   __getitem__ needs idx and returns image + boxes + labels [OK]

Hint: __getitem__ always takes index and returns image with boxes and labels [OK]

Common Mistakes:

• Omitting the idx parameter
• Returning only image and label without boxes
• Using wrong method name without underscores

3. Given this snippet from a custom detection dataset, what will be the output type of dataset[0]?

class CustomDataset(torch.utils.data.Dataset):
    def __getitem__(self, idx):
        image = self.images[idx]
        boxes = self.boxes[idx]
        labels = self.labels[idx]
        target = {"boxes": boxes, "labels": labels}
        return image, target

medium

A. A tuple containing an image tensor and a dictionary with boxes and labels

B. A list containing image, boxes, and labels separately

C. Only the image tensor

D. A dictionary with keys 'image' and 'target'

Solution

1. Step 1: Analyze return statement

   The method returns image, target where target is a dictionary with keys 'boxes' and 'labels'.

2. Step 2: Determine output type

   Returning two items separated by comma means a tuple is returned: (image, target dictionary).

3. Final Answer:

   A tuple containing an image tensor and a dictionary with boxes and labels -> Option A

4. Quick Check:

   Return image, target dict = tuple [OK]

Hint: Comma-separated returns form a tuple in Python [OK]

Common Mistakes:

• Thinking return is a list instead of tuple
• Assuming only image is returned
• Confusing dictionary keys with return structure

4. You wrote this __getitem__ method but get an error when loading data:

def __getitem__(self, idx):
    image = self.images[idx]
    boxes = self.boxes[idx]
    labels = self.labels[idx]
    target = {"boxes": boxes, "labels": labels}
    return image, target

What is the most likely cause of the error?

medium

A. Missing return statement

B. boxes and labels are not converted to tensors

C. image is not normalized

D. Incorrect method name without underscores

Solution

1. Step 1: Check common dataset errors

   Detection models expect bounding boxes and labels as tensors, not raw lists or arrays.

2. Step 2: Identify missing tensor conversion

   If boxes and labels are not converted to tensors, PyTorch will raise errors during training or data loading.

3. Final Answer:

   boxes and labels are not converted to tensors -> Option B

4. Quick Check:

   Boxes and labels must be tensors [OK]

Hint: Always convert boxes and labels to tensors before returning [OK]

Common Mistakes:

• Forgetting to convert boxes and labels to torch tensors
• Assuming image normalization fixes tensor errors
• Using wrong method name causing attribute errors

5. You want to add a transform that flips images horizontally and adjusts bounding boxes accordingly in your custom detection dataset. Which approach is best?

hard

A. Apply the horizontal flip transform to both image and bounding boxes inside __getitem__

B. Flip bounding boxes but not the image

C. Apply the transform only after training the model

D. Apply the horizontal flip transform only to the image inside __getitem__, leave boxes unchanged

Solution

1. Step 1: Understand transform requirements

   When flipping images, bounding boxes must be adjusted to match the new image layout.

2. Step 2: Apply transform to both image and boxes

   Applying the transform only to the image or only to boxes causes mismatch and incorrect training data.

3. Final Answer:

   Apply the horizontal flip transform to both image and bounding boxes inside __getitem__ -> Option A

4. Quick Check:

   Flip image and boxes together for correct data [OK]

Hint: Transform image and boxes together to keep alignment [OK]

Common Mistakes:

• Flipping only image or only boxes
• Applying transforms after training
• Ignoring bounding box coordinate updates