Custom object detection dataset in Computer Vision - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is a custom object detection dataset?

A custom object detection dataset is a collection of images labeled with bounding boxes and class names for specific objects you want a model to recognize. It is tailored to your unique needs, not a general dataset.

beginner

Why do we need bounding boxes in object detection datasets?

Bounding boxes show exactly where an object is in an image by drawing a rectangle around it. This helps the model learn to find and locate objects, not just recognize them.

intermediate

Name two common formats for storing object detection annotations.

Two common formats are Pascal VOC (XML files) and COCO (JSON files). Both store bounding box coordinates and class labels but in different structures.

beginner

What is the role of class labels in a custom object detection dataset?

Class labels tell the model what type of object is inside each bounding box, like 'cat' or 'car'. They help the model learn to distinguish between different objects.

intermediate

How can data augmentation help when creating a custom object detection dataset?

Data augmentation creates new images by changing existing ones (like flipping or rotating). This increases dataset size and variety, helping the model learn better and avoid overfitting.

What does a bounding box in object detection represent?

A The file format of the dataset

B The color of an object

C The size of the image

D The location of an object in an image

Bounding boxes mark the exact location of objects in images for detection.

Which file format is commonly used for object detection annotations?

A CSV

B MP4

C Pascal VOC (XML)

D TXT

Pascal VOC uses XML files to store bounding boxes and labels.

Why is labeling images important in a custom object detection dataset?

A To tell the model what objects to detect

B To improve image resolution

C To compress the dataset

D To change image colors

Labels teach the model which objects to find and recognize.

What is a benefit of data augmentation in object detection datasets?

A Reduces dataset size

B Increases dataset variety

C Removes labels

D Changes file format

Augmentation creates more varied images to help the model learn better.

Which of these is NOT part of a typical object detection annotation?

A Image resolution

B Class label

C Bounding box coordinates

D Object location

Image resolution is metadata, not part of the annotation itself.

Explain the steps to create a custom object detection dataset from scratch.

Think about how you prepare images and labels for training.

Describe why accurate labeling is crucial for training an object detection model.

Consider how the model uses the labels during learning.

Practice

1. What is the main purpose of creating a custom object detection dataset?

easy

A. To compress images for faster loading

B. To improve image resolution automatically

C. To generate random images for testing

D. To teach a computer to find specific objects in images

Solution

1. Step 1: Understand the goal of object detection datasets

   Custom object detection datasets are made to help computers learn to recognize and locate specific objects in images.

2. Step 2: Identify the correct purpose

   Among the options, only teaching a computer to find specific objects matches the goal of such datasets.

3. Final Answer:

   To teach a computer to find specific objects in images -> Option D

4. Quick Check:

   Custom dataset purpose = teach detection [OK]

Hint: Custom datasets train models to detect your objects [OK]

Common Mistakes:

• Confusing dataset purpose with image enhancement
• Thinking dataset creates images instead of labeling
• Assuming dataset compresses images

2. Which of the following is the correct format for an annotation line in a YOLO custom dataset label file?

easy

A. class_id x_center y_center width height (all normalized between 0 and 1)

B. image_width image_height class_id x y (pixel values)

C. class_name x_min y_min x_max y_max (absolute pixel coordinates)

D. class_id x_min y_min width height (normalized coordinates)

Solution

1. Step 1: Recall YOLO annotation format

   YOLO uses normalized coordinates with class_id, x_center, y_center, width, and height all between 0 and 1.

2. Step 2: Match options to YOLO format

   class_id x_center y_center width height (all normalized between 0 and 1) matches this format exactly; others use pixel values or different coordinate types.

3. Final Answer:

   class_id x_center y_center width height (all normalized between 0 and 1) -> Option A

4. Quick Check:

   YOLO labels use normalized center coords [OK]

Hint: YOLO labels use normalized center coordinates [OK]

Common Mistakes:

• Using pixel coordinates instead of normalized
• Confusing bounding box corners with center format
• Mixing class name with class id

3. Given this label file line for an image of size 640x480:
2 0.5 0.5 0.25 0.25
What are the pixel coordinates of the bounding box corners (x_min, y_min, x_max, y_max)?

medium

A. (200, 150, 360, 270)

B. (160, 120, 320, 240)

C. (240, 180, 400, 300)

D. (320, 240, 480, 360)

Solution

1. Step 1: Calculate center pixel coordinates

   x_center = 0.5 * 640 = 320, y_center = 0.5 * 480 = 240

2. Step 2: Calculate width and height in pixels

   width = 0.25 * 640 = 160, height = 0.25 * 480 = 120

3. Step 3: Calculate bounding box corners

   x_min = 320 - 160/2 = 240, y_min = 240 - 120/2 = 180
   x_max = 320 + 160/2 = 400, y_max = 240 + 120/2 = 300

4. Final Answer:

   (240, 180, 400, 300) -> Option C

5. Quick Check:

   Center ± half width/height = corners [OK]

Hint: Multiply normalized by image size, then subtract/add half box [OK]

Common Mistakes:

• Using width/height as full coordinates instead of half
• Mixing up x and y calculations
• Forgetting to multiply by image dimensions

4. You have a dataset folder with images and label files. The labels are missing for some images. What is the best way to fix this before training?

medium

A. Remove images without label files from the dataset

B. Create empty label files for those images

C. Rename images to match existing label files

D. Ignore missing labels and train anyway

Solution

1. Step 1: Understand dataset consistency needs

   Training requires each image to have a matching label file with annotations.

2. Step 2: Choose the best fix

   Removing images without labels avoids errors and ensures clean training data. Creating empty labels may confuse the model, renaming won't help if labels don't exist, ignoring missing labels causes errors.

3. Final Answer:

   Remove images without label files from the dataset -> Option A

4. Quick Check:

   Dataset must have matching image-label pairs [OK]

Hint: Keep only images with labels to avoid training errors [OK]

Common Mistakes:

• Creating empty label files that confuse training
• Ignoring missing labels causing crashes
• Renaming files without creating labels

5. You want to add a new object class to your existing custom dataset. Which steps should you follow to update your dataset correctly?

hard

A. Only add new images with the new class, no config changes needed

B. Add the new class to the class list, label new images with it, and update training config

C. Replace old class labels with the new class in all files

D. Add new class images but keep old labels unchanged without config update

Solution

1. Step 1: Update class list

   Add the new object class to your dataset's class list file so the model knows about it.

2. Step 2: Label new images with the new class

   Annotate images containing the new object using the new class id in label files.

3. Step 3: Update training configuration

   Modify your training config to include the new class count and class names.

4. Final Answer:

   Add the new class to the class list, label new images with it, and update training config -> Option B

5. Quick Check:

   Update classes, labels, and config for new objects [OK]

Hint: Update classes, label new data, and config for new objects [OK]

Common Mistakes:

• Not updating class list causing training errors
• Forgetting to label new objects
• Ignoring config updates leading to wrong class counts