CV project workflow in Computer Vision - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

Key Step Identification in CV Project Workflow

Which step is typically the first in a computer vision project workflow?

A Training the model

B Collecting and labeling the dataset

C Evaluating the model performance

D Deploying the model to production

💡 Hint

Think about what you need before training a model.

The first step is to collect and label the dataset. Without data, you cannot train or evaluate a model.

❓ Model Choice

intermediate

Choosing a Model Architecture for Image Classification

You want to classify images into 10 categories. Which model architecture is most suitable to start with?

A Convolutional Neural Network (CNN)

B Recurrent Neural Network (RNN)

C Linear Regression

D K-Means Clustering

💡 Hint

Consider which model type is designed to process images.

CNNs are designed to handle image data by capturing spatial features. RNNs are for sequences, linear regression is for numeric prediction, and K-Means is unsupervised clustering.

❓ Metrics

advanced

Evaluating Model Performance with Accuracy and Confusion Matrix

After training a CV model, you get the following confusion matrix for 3 classes:

[[50, 2, 3], [4, 45, 1], [2, 3, 48]]

What is the overall accuracy?

A 0.91

B 0.86

C 0.95

D 0.89

💡 Hint

Accuracy = (sum of diagonal) / (sum of all elements).

Sum diagonal = 50+45+48=143; total sum = 50+2+3+4+45+1+2+3+48=158; accuracy = 143/158 ≈ 0.91.

🔧 Debug

advanced

Identifying the Bug in Data Augmentation Code

What error will this code raise when applying data augmentation using PyTorch transforms?

import torchvision.transforms as T
from PIL import Image
transform = T.Compose([
    T.RandomHorizontalFlip(p=0.5),
    T.ToTensor(),
    T.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
])

image = Image.open('image.jpg')
augmented = transform(image)

A AttributeError: 'Image' object has no attribute 'shape'

B RuntimeError: image mode not supported

C No error, code runs successfully

D TypeError: normalize expects 3 channels but got 1

💡 Hint

Check the number of channels in the image and the mean/std length.

The image is likely RGB (3 channels), so mean and std should have 3 values each to match the channels, avoiding errors in Normalize.

❓ Hyperparameter

expert

Effect of Learning Rate on Training Stability

You train a deep CNN for object detection. Which learning rate choice is most likely to cause unstable training with loss oscillations?

A 0.0001

B 0.001

C 0.0005

D 0.01

💡 Hint

Higher learning rates can cause unstable updates.

A learning rate of 0.01 is often too high for deep CNNs, causing large weight updates and oscillating loss.

Practice

1. Which step comes first in a typical computer vision project workflow?

easy

A. Monitor model performance

B. Deploy the model to production

C. Tune hyperparameters

D. Define the problem and collect data

Solution

1. Step 1: Understand the project start

   The first step is to clearly define what problem you want to solve and gather the images or videos needed.

2. Step 2: Recognize the order of workflow steps

   Data collection must happen before training, tuning, or deployment.

3. Final Answer:

   Define the problem and collect data -> Option D

4. Quick Check:

   First step = Define problem and collect data [OK]

Hint: Start with problem definition and data collection [OK]

Common Mistakes:

• Thinking deployment is the first step
• Skipping problem definition
• Ignoring data collection importance

2. Which of the following is the correct syntax to split data into training and testing sets in Python using scikit-learn?

easy

A. train_test_split(data, test_size=0.2)

B. split_train_test(data, 0.2)

C. train_test(data, test=0.2)

D. train_test_split(data, test=0.2)

Solution

1. Step 1: Recall scikit-learn function name and parameters

   The correct function is train_test_split with parameter test_size to specify test data fraction.

2. Step 2: Check parameter correctness

   train_test_split(data, test_size=0.2) uses correct function and parameter names.

3. Final Answer:

   train_test_split(data, test_size=0.2) -> Option A

4. Quick Check:

   Correct function and parameter = train_test_split(data, test_size=0.2) [OK]

Hint: Remember exact function and parameter names from scikit-learn [OK]

Common Mistakes:

• Using wrong function name
• Using incorrect parameter names
• Confusing test_size with test

3. Given this code snippet for training a simple CNN model, what will be the printed output after training for 1 epoch?

import tensorflow as tf
model = tf.keras.Sequential([
  tf.keras.layers.Conv2D(16, 3, activation='relu', input_shape=(28,28,1)),
  tf.keras.layers.Flatten(),
  tf.keras.layers.Dense(10, activation='softmax')
])
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
history = model.fit(x_train, y_train, epochs=1, batch_size=32)
print(history.history['accuracy'][0])

medium

A. An error because 'accuracy' is not in history

B. The loss value after training

C. A float value between 0 and 1 representing training accuracy

D. The number of training samples

Solution

1. Step 1: Understand model.fit output

   The history object stores metrics per epoch. Accessing history.history['accuracy'][0] gives training accuracy after first epoch.

2. Step 2: Confirm metric requested

   Since metrics=['accuracy'] was set, accuracy is recorded and printed as a float between 0 and 1.

3. Final Answer:

   A float value between 0 and 1 representing training accuracy -> Option C

4. Quick Check:

   history.history['accuracy'][0] = training accuracy [OK]

Hint: history.history['accuracy'][0] holds first epoch accuracy [OK]

Common Mistakes:

• Confusing accuracy with loss
• Expecting an error accessing accuracy
• Thinking it prints sample count

4. You trained a model but it performs poorly on new images. Which step in the workflow might be causing this issue?

medium

A. Monitoring was set up correctly

B. Data preparation was insufficient or incorrect

C. Hyperparameters were tuned perfectly

D. Model deployment was done too early

Solution

1. Step 1: Analyze poor model performance cause

   Poor results on new data often mean the model did not learn well, usually due to bad or insufficient data preparation.

2. Step 2: Eliminate unrelated options

   Deployment timing, perfect hyperparameters, or monitoring setup do not directly cause poor initial performance.

3. Final Answer:

   Data preparation was insufficient or incorrect -> Option B

4. Quick Check:

   Poor performance = bad data prep [OK]

Hint: Check data prep first when model fails on new data [OK]

Common Mistakes:

• Blaming deployment timing
• Assuming hyperparameters are always perfect
• Ignoring data quality issues

5. In a computer vision project, after deploying your model, you notice accuracy drops over time. What is the best next step to maintain model performance?

hard

A. Collect new data and retrain the model regularly

B. Stop monitoring since model is deployed

C. Reduce the size of the training dataset

D. Ignore the drop as normal and do nothing

Solution

1. Step 1: Understand model drift after deployment

   Models can lose accuracy as data changes. Collecting new data and retraining helps adapt to changes.

2. Step 2: Evaluate other options

   Stopping monitoring or ignoring drops will worsen performance. Reducing training data size is counterproductive.

3. Final Answer:

   Collect new data and retrain the model regularly -> Option A

4. Quick Check:

   Maintain performance = retrain with new data [OK]

Hint: Retrain model regularly with fresh data after deployment [OK]

Common Mistakes:

• Ignoring monitoring after deployment
• Reducing training data size
• Assuming model never needs updates