CNN architecture review in Computer Vision - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is the main purpose of convolutional layers in a CNN?

Convolutional layers detect local patterns like edges or textures by sliding small filters over the input image, helping the model learn important features.

beginner

Why do CNNs use pooling layers?

Pooling layers reduce the size of feature maps, making the model faster and less sensitive to small shifts or distortions in the input.

beginner

What role do fully connected layers play in a CNN?

Fully connected layers combine all extracted features to make final predictions, like classifying the image into categories.

intermediate

Explain the concept of 'stride' in convolutional layers.

Stride is how many pixels the filter moves at each step when sliding over the input. Larger strides reduce output size but may skip details.

intermediate

What is the benefit of using multiple convolutional layers stacked together?

Stacking layers lets the CNN learn complex features step-by-step, from simple edges in early layers to detailed shapes in deeper layers.

What does a convolutional filter primarily detect in an image?

A The entire image color

B Local patterns like edges

C Random noise

D Image file size

Convolutional filters scan small parts of the image to detect local patterns such as edges or textures.

Which layer type reduces the spatial size of feature maps in a CNN?

A Dropout layer

B Fully connected layer

C Pooling layer

D Convolutional layer

Pooling layers reduce the size of feature maps to make the model faster and more robust.

What does the 'stride' parameter control in a convolutional layer?

A The number of filters

B The learning rate

C The color channels

D How far the filter moves each step

Stride controls how many pixels the filter moves when sliding over the input.

Why are multiple convolutional layers stacked in a CNN?

A To learn complex features progressively

B To increase image size

C To reduce training data

D To avoid overfitting

Stacking layers helps the model learn from simple to complex features step-by-step.

What is the main function of fully connected layers in CNNs?

A Combine features for final prediction

B Detect edges in images

C Reduce image size

D Normalize input data

Fully connected layers use all extracted features to make the final classification or prediction.

Describe the main components of a CNN architecture and their roles.

Think about how the model processes images step-by-step.

Explain why stacking multiple convolutional layers helps a CNN learn better.

Consider how deeper layers build on earlier layers' outputs.

Practice

1. What is the main purpose of a Convolutional Neural Network (CNN) in computer vision?

easy

A. To perform text translation

B. To sort numbers in a list

C. To generate random images

D. To detect patterns and features in images

Solution

1. Step 1: Understand CNN function

   CNNs scan images to find important patterns like edges and shapes.

2. Step 2: Match purpose to options

   Only To detect patterns and features in images describes detecting patterns in images, which is CNN's main job.

3. Final Answer:

   To detect patterns and features in images -> Option D

4. Quick Check:

   CNN purpose = detect image patterns [OK]

Hint: CNNs find image features, not unrelated tasks like sorting [OK]

Common Mistakes:

• Confusing CNNs with general neural networks
• Thinking CNNs generate images
• Mixing CNNs with text processing models

2. Which of the following is the correct way to add a 2D convolutional layer in Keras?

easy

A. Dense(units=32, activation='relu')

B. Conv1D(filters=32, kernel_size=3, activation='relu')

C. Conv2D(filters=32, kernel_size=(3,3), activation='relu')

D. MaxPooling2D(pool_size=(2,2))

Solution

1. Step 1: Identify Conv2D syntax

   Conv2D requires filters, kernel_size as a tuple, and activation function.

2. Step 2: Compare options

   Conv2D(filters=32, kernel_size=(3,3), activation='relu') matches Conv2D syntax correctly; others are different layers or wrong dimensions.

3. Final Answer:

   Conv2D(filters=32, kernel_size=(3,3), activation='relu') -> Option C

4. Quick Check:

   Conv2D syntax = Conv2D(filters=32, kernel_size=(3,3), activation='relu') [OK]

Hint: Conv2D uses 2D kernel size tuple, not single int [OK]

Common Mistakes:

• Using Conv1D instead of Conv2D for images
• Confusing Dense layer with Conv2D
• Wrong kernel_size format

3. Given this Keras CNN snippet, what is the output shape after the Conv2D layer?

model = Sequential()
model.add(Conv2D(16, (3,3), input_shape=(28,28,1)))

medium

A. (26, 26, 16)

B. (28, 28, 16)

C. (30, 30, 16)

D. (28, 28, 1)

Solution

1. Step 1: Calculate output size after Conv2D

   With default 'valid' padding and kernel size 3, output dims = input - kernel + 1 = 28 - 3 + 1 = 26.

2. Step 2: Determine output channels

   Filters=16 means output depth is 16 channels.

3. Final Answer:

   (26, 26, 16) -> Option A

4. Quick Check:

   Output shape = (26,26,16) [OK]

Hint: Output size = input - kernel + 1 with 'valid' padding [OK]

Common Mistakes:

• Assuming output size equals input size without padding
• Confusing number of filters with spatial dimensions
• Forgetting default padding is 'valid'

4. Identify the error in this CNN model code snippet:

model = Sequential()
model.add(Conv2D(32, (3,3), activation='relu', input_shape=(28,28)))
model.add(Flatten())
model.add(Dense(10, activation='softmax'))

medium

A. Dense layer should come before Flatten

B. input_shape missing channel dimension

C. Activation function 'relu' is invalid

D. Conv2D filters must be 64 or more

Solution

1. Step 1: Check input_shape format

   Conv2D expects input_shape with 3 dimensions: height, width, channels. Here channels are missing.

2. Step 2: Validate other parts

   Activation 'relu' is valid, Flatten before Dense is correct, filters can be any positive integer.

3. Final Answer:

   input_shape missing channel dimension -> Option B

4. Quick Check:

   Input shape must include channels [OK]

Hint: Conv2D input_shape needs (height, width, channels) [OK]

Common Mistakes:

• Ignoring channel dimension in input_shape
• Misordering Flatten and Dense layers
• Thinking filters must be >=64

5. You want to build a CNN for classifying 64x64 RGB images into 5 classes. Which architecture choice is best?

hard

A. Conv2D(32, (3,3)) + MaxPooling2D + Conv2D(64, (3,3)) + Flatten + Dense(5, softmax)

B. Dense(128) + Dense(64) + Dense(5, softmax)

C. Conv1D(32, 3) + Flatten + Dense(5, softmax)

D. Flatten + Dense(5, softmax)

Solution

1. Step 1: Identify suitable layers for image data

   Conv2D layers extract spatial features from 2D images; MaxPooling reduces size; Flatten prepares for Dense.

2. Step 2: Evaluate options

   Conv2D(32, (3,3)) + MaxPooling2D + Conv2D(64, (3,3)) + Flatten + Dense(5, softmax) uses Conv2D and pooling correctly for images. The Dense-only option lacks feature extraction, Conv1D is unsuitable for 2D images, and Flatten + Dense skips convolutions.

3. Final Answer:

   Conv2D(32, (3,3)) + MaxPooling2D + Conv2D(64, (3,3)) + Flatten + Dense(5, softmax) -> Option A

4. Quick Check:

   Use Conv2D + pooling for images [OK]

Hint: Use Conv2D layers for images, not Dense-only or Conv1D [OK]

Common Mistakes:

• Using Dense layers only for image input
• Applying Conv1D to 2D images
• Skipping pooling layers for downsampling