Dataset bias in vision in Computer Vision - Model Metrics & Evaluation

Metrics & Evaluation - Dataset bias in vision

Which metric matters for Dataset Bias in Vision and WHY

When dealing with dataset bias in vision tasks, accuracy alone can be misleading. Instead, precision, recall, and F1 score are important to understand how well the model performs across different groups or classes. This helps reveal if the model favors some categories over others due to bias in the data.

Also, confusion matrices help visualize errors per class, showing if some classes are systematically misclassified because of bias.

Confusion Matrix Example

      Actual \ Predicted | Cat | Dog | Rabbit
      -------------------------------------
      Cat                | 45  |  5  |  0
      Dog                | 10  | 30  | 10
      Rabbit             |  0  |  5  | 40
    

This matrix shows the model predicts cats well but confuses dogs and rabbits more. If the dataset had fewer rabbit images, the model might be biased against rabbits.

Precision vs Recall Tradeoff with Dataset Bias

Imagine a vision model detecting rare animals. If the dataset has few examples of rare animals, the model might have:

• High precision but low recall for rare animals: It only predicts rare animals when very sure, missing many actual rare animals.
• High recall but low precision: It predicts many rare animals, but many are wrong.

Dataset bias often causes low recall for underrepresented classes, meaning the model misses many true cases.

Good vs Bad Metric Values for Dataset Bias in Vision

Good: Balanced precision and recall across all classes, showing the model treats all categories fairly.

Bad: Very high accuracy but very low recall on minority classes, indicating the model ignores or misclassifies those classes due to bias.

Common Pitfalls in Metrics Due to Dataset Bias

• Accuracy paradox: High overall accuracy hides poor performance on minority classes.
• Data leakage: If biased features leak into training, metrics may look good but fail in real use.
• Overfitting to majority class: Model memorizes common classes, ignoring rare ones.

Self Check

Your vision model has 98% accuracy but only 12% recall on a rare animal class. Is it good for production?

Answer: No. The model misses most rare animals, which is critical if detecting them matters. High accuracy is misleading because the rare class is small but important.

Key Result

Balanced precision and recall across classes reveal dataset bias better than accuracy alone.

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