CV project workflow in Computer Vision - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is the first step in a computer vision project workflow?

The first step is to clearly define the problem you want to solve, such as object detection, image classification, or segmentation.

beginner

Why is data collection important in a CV project?

Data collection provides the images or videos needed to train and test the model. Good quality and diverse data help the model learn better.

intermediate

What does data preprocessing involve in a CV workflow?

Data preprocessing includes resizing images, normalizing pixel values, augmenting data, and labeling images to prepare them for training.

beginner

What is the purpose of model training in a CV project?

Model training teaches the computer vision model to recognize patterns in the data by adjusting its parameters to minimize errors.

intermediate

How do you evaluate a computer vision model's performance?

You evaluate it using metrics like accuracy, precision, recall, or IoU (Intersection over Union) depending on the task, using a separate test dataset.

What is the main goal of data augmentation in CV projects?

A Label the images automatically

B Reduce the size of the dataset

C Remove noisy images

D Increase the size and diversity of the training data

Data augmentation creates new training examples by modifying existing images, helping the model generalize better.

Which step comes directly after model training in a typical CV workflow?

A Data collection

B Data preprocessing

C Model evaluation

D Problem definition

After training, the model is evaluated to check how well it performs on unseen data.

Why do we split data into training and test sets?

A To evaluate model performance on unseen data

B To train two different models

C To reduce data size

D To label data automatically

Splitting data helps test if the model can generalize beyond the data it learned from.

Which metric is commonly used for object detection tasks?

A Accuracy

B IoU (Intersection over Union)

C Mean Squared Error

D Perplexity

IoU measures how well predicted bounding boxes overlap with ground truth boxes in object detection.

What is the role of labeling in a CV project?

A To assign correct answers to images for supervised learning

B To organize files

C To compress images

D To increase image resolution

Labeling provides the correct answers the model learns to predict during training.

Describe the main steps in a computer vision project workflow from start to finish.

Think about what you do first, how you prepare data, then how you teach and test the model.

Explain why data preprocessing and augmentation are important before training a CV model.

Consider how raw images might vary and how the model benefits from more varied examples.

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