Dataset bias in vision in Computer Vision - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is dataset bias in vision?

Dataset bias in vision happens when the images or data used to train a model do not represent the real world well. This causes the model to perform poorly on new or different images.

beginner

Why is dataset bias a problem for computer vision models?

Because biased datasets make models learn wrong or incomplete patterns. This leads to errors when the model sees new images that are different from the training data.

beginner

Name one common cause of dataset bias in vision datasets.

One common cause is collecting images from limited sources or environments, like only indoor photos or only one type of camera, which limits diversity.

intermediate

How can we reduce dataset bias in vision projects?

We can reduce bias by collecting diverse images from many sources, using data augmentation, and testing models on different datasets to check fairness.

intermediate

What is an example of dataset bias affecting a vision model in real life?

A face recognition system trained mostly on light-skinned faces may fail to recognize dark-skinned faces well, showing bias from the training data.

What does dataset bias in vision mainly affect?

A Size of the dataset

B Speed of model training

C Model's ability to generalize to new images

D Color of images

Dataset bias affects how well a model can apply what it learned to new, unseen images.

Which of these is a cause of dataset bias?

A Collecting images only from one camera type

B Increasing dataset size

C Adding noise to images

D Using images from many different environments

Using images from only one camera type limits diversity and can cause bias.

How can data augmentation help with dataset bias?

A By creating more diverse images from existing ones

B By removing images from the dataset

C By speeding up training

D By reducing image resolution

Data augmentation creates variations of images to increase diversity and reduce bias.

What is a sign that a vision model suffers from dataset bias?

A It performs well on all types of images

B It performs poorly on images different from training data

C It trains very fast

D It uses a lot of memory

Poor performance on new or different images indicates dataset bias.

Which approach helps test if a vision model is biased?

A Testing on the same dataset used for training

B Using fewer images

C Training longer

D Testing on a different, diverse dataset

Testing on a different dataset shows if the model generalizes well or is biased.

Explain what dataset bias in vision is and why it matters.

Think about how training data affects what the model learns.

Describe methods to identify and reduce dataset bias in vision datasets.

Consider both checking and fixing bias.

Practice

1. What does dataset bias in computer vision mean?

easy

A. The data does not fairly represent all types of images or cases

B. The model always predicts perfectly on all images

C. The dataset is too large to process

D. The images are all black and white

Solution

1. Step 1: Understand dataset bias meaning

   Dataset bias means the data used to train a model does not cover all possible cases fairly.

2. Step 2: Compare options to definition

   Only The data does not fairly represent all types of images or cases describes this correctly. Other options describe unrelated issues.

3. Final Answer:

   The data does not fairly represent all types of images or cases -> Option A

4. Quick Check:

   Dataset bias = unfair data representation [OK]

Hint: Bias means data is not fair or balanced [OK]

Common Mistakes:

• Thinking bias means model is perfect
• Confusing bias with dataset size
• Assuming bias means image color

2. Which of the following is the correct way to check for dataset bias in a vision dataset using Python?

easy

A. Use random.shuffle(dataset) to fix bias

B. Use value_counts() on labels to see class distribution

C. Use len(dataset) without checking labels

D. Use print(dataset) only

Solution

1. Step 1: Identify method to check bias

   Checking class distribution with value_counts() helps find imbalance in labels.

2. Step 2: Evaluate other options

   Printing dataset or length alone doesn't show bias. Shuffling data doesn't check bias.

3. Final Answer:

   Use value_counts() on labels to see class distribution -> Option B

4. Quick Check:

   Check label counts = value_counts() [OK]

Hint: Check label counts to find bias [OK]

Common Mistakes:

• Only printing dataset without analysis
• Assuming dataset length shows bias
• Thinking shuffling fixes bias

3. Given this Python code snippet analyzing a vision dataset labels:

import pandas as pd
labels = ['cat', 'dog', 'cat', 'cat', 'dog', 'bird']
label_counts = pd.Series(labels).value_counts()
print(label_counts)

What is the output?

medium

A. bird 3
cat 2
dog 1

B. cat 2
dog 3
bird 1

C. cat 3
dog 2
bird 1

D. dog 3
cat 3
bird 3

Solution

1. Step 1: Count occurrences of each label

   Labels list has 'cat' 3 times, 'dog' 2 times, and 'bird' 1 time.

2. Step 2: Understand value_counts output

   value_counts() returns counts sorted descending by default.

3. Final Answer:

   cat 3\ndog 2\nbird 1 -> Option C

4. Quick Check:

   Count labels correctly = cat:3, dog:2, bird:1 [OK]

Hint: Count each label frequency carefully [OK]

Common Mistakes:

• Mixing counts of dog and cat
• Assuming alphabetical order instead of count order
• Miscounting occurrences

4. You have this code to check dataset bias:

labels = ['car', 'car', 'truck', 'car', 'truck']
counts = {}
for label in labels:
    counts[label] = counts.get(label, 0) + 1
print(counts)

But the output is {}. What is the likely error?

medium

A. The 'get' method is used incorrectly with wrong parameters

B. The print statement is outside the loop and missing indentation

C. The code is correct; output should be {'car': 3, 'truck': 2}

D. The dictionary 'counts' was reinitialized inside the loop

Solution

1. Step 1: Analyze code behavior

   If 'counts' is reset inside the loop, it will be empty after loop ends.

2. Step 2: Identify cause of empty output

   Reinitializing 'counts' inside loop clears previous counts, causing empty dict at print.

3. Final Answer:

   The dictionary 'counts' was reinitialized inside the loop -> Option D

4. Quick Check:

   Resetting dict inside loop empties counts [OK]

Hint: Check if dict is reset inside loop [OK]

Common Mistakes:

• Thinking print indentation causes empty output
• Assuming get() method is wrong
• Ignoring variable scope inside loop

5. You have a vision dataset with 90% images of cats and 10% dogs. Which method best reduces dataset bias to improve model fairness?

hard

A. Collect more dog images to balance classes

B. Remove all cat images to keep only dogs

C. Train model only on cat images

D. Ignore class imbalance and train as is

Solution

1. Step 1: Understand dataset imbalance problem

   Having 90% cats and 10% dogs causes bias favoring cats.

2. Step 2: Choose method to fix bias

   Collecting more dog images balances classes, reducing bias and improving fairness.

3. Final Answer:

   Collect more dog images to balance classes -> Option A

4. Quick Check:

   Balance classes by adding data [OK]

Hint: Balance classes by adding underrepresented data [OK]

Common Mistakes:

• Removing majority class loses useful data
• Training on one class ignores others
• Ignoring imbalance causes biased model