Evaluation and confusion matrix in Computer Vision - Model Pipeline Trace

Model Pipeline - Evaluation and confusion matrix

This pipeline shows how a computer vision model is evaluated using a confusion matrix. It helps us understand how well the model predicts different classes by comparing predictions to true labels.

Data Flow - 5 Stages

1Input Images

1000 images x 64x64 pixels x 3 color channels→Raw images loaded for classification→1000 images x 64x64 pixels x 3 color channels

Image of a cat, Image of a dog, Image of a bird

↓

2Preprocessing

1000 images x 64x64 x 3→Resize and normalize pixel values (0-1)→1000 images x 64x64 x 3

Pixel values scaled from 0-255 to 0-1

↓

3Feature Extraction

1000 images x 64x64 x 3→Extract features using CNN layers→1000 samples x 128 features

Feature vector representing edges, textures

↓

4Model Prediction

1000 samples x 128 features→Fully connected layers output class probabilities→1000 samples x 3 classes

[0.7, 0.2, 0.1] for cat, dog, bird

↓

5Evaluation

1000 samples x 3 classes→Compare predicted classes to true labels→Confusion matrix 3x3

Matrix showing counts of true vs predicted classes

Training Trace - Epoch by Epoch

Loss
1.2 |*       
0.9 | *      
0.7 |  *     
0.5 |   *    
0.4 |    *   
    +---------
     1 2 3 4 5 Epochs

Epoch	Loss ↓	Accuracy ↑	Observation
1	1.2	0.45	Model starts learning, accuracy low
2	0.9	0.60	Loss decreases, accuracy improves
3	0.7	0.72	Model learns better features
4	0.5	0.80	Good improvement in accuracy
5	0.4	0.85	Model converges with good accuracy

Prediction Trace - 5 Layers

Layer 1: Input Image

Layer 2: Feature Extraction (CNN)

Layer 3: Fully Connected Layer

Layer 4: Prediction

Layer 5: Confusion Matrix Update

Model Quiz - 3 Questions

What does a confusion matrix show in model evaluation?

A Counts of true vs predicted classes

B The model's training loss over epochs

C The input image sizes

D The number of features extracted

Key Insight

The confusion matrix is a simple but powerful tool to see where a model makes mistakes. It helps us understand if the model confuses certain classes more than others, guiding improvements.

Practice

1. What does a confusion matrix help you understand in a classification model?

easy

A. The speed of the model during training

B. How well the model predicts each class by showing true and false predictions

C. The number of layers in the model

D. The size of the input images

Solution

1. Step 1: Understand the purpose of a confusion matrix

   A confusion matrix shows counts of correct and incorrect predictions for each class, helping evaluate classification performance.

2. Step 2: Match the description to the options

   Only How well the model predicts each class by showing true and false predictions describes this purpose correctly, while others relate to unrelated model aspects.

3. Final Answer:

   How well the model predicts each class by showing true and false predictions -> Option B

4. Quick Check:

   Confusion matrix = True/False predictions summary [OK]

Hint: Confusion matrix shows correct vs wrong class predictions [OK]

Common Mistakes:

• Confusing confusion matrix with model speed
• Thinking it shows model architecture details
• Assuming it shows input data size

2. Which of the following is the correct way to create a confusion matrix using scikit-learn in Python?

easy

A. confusion_matrix(y_pred)

B. confusionMatrix(y_true, y_pred)

C. conf_matrix(y_pred, y_true)

D. confusion_matrix(y_true, y_pred)

Solution

1. Step 1: Recall the scikit-learn function signature

   The function to create a confusion matrix is confusion_matrix(y_true, y_pred) with true labels first, then predicted labels.

2. Step 2: Check each option for correctness

   confusion_matrix(y_true, y_pred) matches the correct function and argument order. Options B, C, and D have wrong names or argument orders.

3. Final Answer:

   confusion_matrix(y_true, y_pred) -> Option D

4. Quick Check:

   Correct function name and argument order [OK]

Hint: Use exact function name and order: confusion_matrix(true, pred) [OK]

Common Mistakes:

• Using wrong function name capitalization
• Swapping true and predicted labels
• Passing only one argument

3. Given the following code, what will be the output confusion matrix?

from sklearn.metrics import confusion_matrix

y_true = [0, 1, 0, 1, 0, 1, 1]
y_pred = [0, 0, 0, 1, 0, 1, 1]

cm = confusion_matrix(y_true, y_pred)
print(cm)

medium

A. [[3 0]
[1 3]]

B. [[2 1]
[0 4]]

C. [[3 1]
[0 3]]

D. [[4 0]
[1 2]]

Solution

1. Step 1: Count true positives and negatives

   Class 0 true positives: y_true=0 and y_pred=0 occur 3 times; false negatives: y_true=1 but y_pred=0 occur once.

2. Step 2: Build confusion matrix

   Matrix rows = true labels, columns = predicted labels. So cm = [[3,0],[1,3]] matches counts.

3. Final Answer:

   [[3 0] [1 3]] -> Option A

4. Quick Check:

   Count matches matrix entries [OK]

Hint: Count true/pred pairs carefully to fill matrix [OK]

Common Mistakes:

• Mixing rows and columns order
• Counting predicted labels as true labels
• Ignoring zero counts

4. You wrote this code but got an error:

from sklearn.metrics import confusion_matrix

cm = confusion_matrix(y_pred, y_true)
print(cm)

What is the likely cause of the error?

medium

A. Using print instead of return

B. Missing import statement for confusion_matrix

C. Swapped y_pred and y_true arguments causing shape mismatch

D. y_pred and y_true are not defined variables

Solution

1. Step 1: Check argument order for confusion_matrix

   The function expects y_true first, then y_pred. Swapping them can cause errors or wrong results.

2. Step 2: Analyze the error cause

   Since import is present and print is valid, the likely cause is swapped arguments causing shape or value errors.

3. Final Answer:

   Swapped y_pred and y_true arguments causing shape mismatch -> Option C

4. Quick Check:

   Correct argument order is true labels first [OK]

Hint: Always pass true labels first, predicted second [OK]

Common Mistakes:

• Swapping true and predicted labels
• Forgetting to import confusion_matrix
• Using undefined variables

5. You have a 3-class image classifier with classes A, B, and C. The confusion matrix is:

[[5 2 0]
 [1 7 1]
 [0 2 6]]

What is the precision for class B?

hard

A. 7 / (2 + 7 + 2) = 0.58

B. 7 / (1 + 7 + 1) = 0.7

C. 7 / (5 + 1 + 0) = 0.7

D. 7 / (7 + 1 + 2) = 0.58

Solution

1. Step 1: Identify precision formula for class B

   Precision = True Positives for B / (All predicted as B). True Positives = cm[1][1] = 7.

2. Step 2: Calculate total predicted as B

   Sum column 1: cm[0][1]=2 + cm[1][1]=7 + cm[2][1]=2 = 11. So precision = 7/11 ≈ 0.636, closest to 0.58 in 7 / (2 + 7 + 2) = 0.58.

3. Final Answer:

   7 / (2 + 7 + 2) = 0.58 -> Option A

4. Quick Check:

   Precision = TP / predicted positives [OK]

Hint: Precision = TP / sum of predicted class column [OK]

Common Mistakes:

• Using row sums instead of column sums
• Confusing precision with recall
• Ignoring off-diagonal values in predicted class column