Architecture search concepts in Computer Vision - Model Pipeline Trace

Model Pipeline - Architecture search concepts

Architecture search helps find the best design for a computer vision model automatically. It tries different model structures to improve accuracy and efficiency.

Data Flow - 5 Stages

1Raw Image Data

1000 images x 64 x 64 x 3→Collect original images with height 64, width 64, and 3 color channels→1000 images x 64 x 64 x 3

Image of a cat with 64x64 pixels and RGB colors

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2Preprocessing

1000 images x 64 x 64 x 3→Normalize pixel values to range 0-1→1000 images x 64 x 64 x 3

Pixel values changed from 0-255 to 0.0-1.0

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3Architecture Search Setup

1000 images x 64 x 64 x 3→Define search space of possible model layers and connections→Search space with multiple candidate architectures

Options include different numbers of convolution layers and filter sizes

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4Model Training per Candidate

Candidate architecture + training data→Train each candidate model on training images→Trained model weights and validation accuracy

Model with 3 conv layers trained for 5 epochs

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5Evaluation and Selection

Multiple trained candidate models→Compare validation accuracy and select best architecture→Best performing model architecture

Model with 4 conv layers and 64 filters selected

Training Trace - Epoch by Epoch

Loss: 1.2 |****     
Loss: 0.9 |******   
Loss: 0.7 |******** 
Loss: 0.55|*********
Loss: 0.50|*********

Epoch	Loss ↓	Accuracy ↑	Observation
1	1.2	0.45	Model starts learning basic features
2	0.9	0.60	Accuracy improves as filters learn edges and shapes
3	0.7	0.72	Model captures more complex patterns
4	0.55	0.80	Good convergence, model generalizes better
5	0.50	0.83	Training stabilizes with high accuracy

Prediction Trace - 6 Layers

Layer 1: Input Layer

Layer 2: Convolution Layer 1

Layer 3: Pooling Layer

Layer 4: Convolution Layer 2

Layer 5: Fully Connected Layer

Layer 6: Output Layer with Softmax

Model Quiz - 3 Questions

What is the main goal of architecture search in this pipeline?

A To collect more training images

B To find the best model design automatically

C To normalize pixel values

D To increase image resolution

Key Insight

Architecture search automates finding the best model design by testing many options. This helps improve accuracy and efficiency without manual trial and error.

Practice

1. What is the main goal of architecture search in computer vision models?

easy

A. To collect more training data

B. To manually tune model parameters

C. To automatically find the best model design

D. To reduce image resolution

Solution

1. Step 1: Understand architecture search purpose

   Architecture search aims to find the best model design automatically without manual trial and error.

2. Step 2: Compare options

   Options B, C, and D do not describe architecture search goals. Only To automatically find the best model design matches the goal.

3. Final Answer:

   To automatically find the best model design -> Option C

4. Quick Check:

   Architecture search = automatic best design [OK]

Hint: Architecture search = automatic model design finder [OK]

Common Mistakes:

• Confusing architecture search with data collection
• Thinking it manually tunes parameters
• Mixing it with image preprocessing

2. Which of the following is a correct way to describe a search space in architecture search?

easy

A. A set of possible model designs to explore

B. The training dataset used for the model

C. The final accuracy metric after training

D. The hardware used to run the model

Solution

1. Step 1: Define search space

   Search space is the collection of all possible model designs or configurations that the search will try.

2. Step 2: Eliminate incorrect options

   Options B, C, and D relate to data, metrics, or hardware, not the search space itself.

3. Final Answer:

   A set of possible model designs to explore -> Option A

4. Quick Check:

   Search space = possible designs [OK]

Hint: Search space = all model options to try [OK]

Common Mistakes:

• Confusing search space with dataset
• Thinking search space is a metric
• Mixing search space with hardware details

3. Consider this pseudocode for architecture search:

for model in search_space:
    accuracy = train_and_evaluate(model)
    if accuracy > best_accuracy:
        best_model = model
        best_accuracy = accuracy
print(best_accuracy)

What does this code output?

medium

A. The list of all models tested

B. The accuracy of the best model found

C. The training loss of the last model

D. The total number of models in search_space

Solution

1. Step 1: Analyze the loop

   The loop trains and evaluates each model, updating best_accuracy if current accuracy is higher.

2. Step 2: Understand the print statement

   After checking all models, it prints the highest accuracy found among them.

3. Final Answer:

   The accuracy of the best model found -> Option B

4. Quick Check:

   Prints best accuracy = highest accuracy [OK]

Hint: Code prints highest accuracy found during search [OK]

Common Mistakes:

• Thinking it prints number of models
• Confusing accuracy with loss
• Assuming it prints all models

4. The following code snippet is intended to find the best model architecture, but it has a bug:

best_accuracy = 0
for model in search_space:
    accuracy = train_and_evaluate(model)
    if accuracy < best_accuracy:
        best_model = model
        best_accuracy = accuracy
print(best_accuracy)

What is the bug?

medium

A. best_accuracy should start at 1 instead of 0

B. train_and_evaluate should return loss, not accuracy

C. The print statement should print best_model, not best_accuracy

D. The comparison operator should be > instead of <

Solution

1. Step 1: Understand the goal

   The goal is to find the model with the highest accuracy, so we want to update when accuracy is greater than best_accuracy.

2. Step 2: Identify the bug

   The code uses accuracy < best_accuracy, which updates for worse accuracy, so it should be accuracy > best_accuracy.

3. Final Answer:

   The comparison operator should be > instead of < -> Option D

4. Quick Check:

   Use > to find best accuracy [OK]

Hint: Best accuracy means use >, not < in comparison [OK]

Common Mistakes:

• Starting best_accuracy at wrong value
• Printing wrong variable
• Confusing accuracy with loss

5. You want to speed up architecture search by reducing the search space size. Which strategy is best?

hard

A. Limit model depth and number of layers to a smaller range

B. Increase the number of training epochs for each model

C. Use a slower but more accurate optimizer

D. Train all models on the full dataset without sampling

Solution

1. Step 1: Understand search space impact

   Reducing search space size means limiting the number of possible model designs to try.

2. Step 2: Evaluate options

   Limit model depth and number of layers to a smaller range reduces model complexity range, shrinking search space. Options A, B, and D increase training time or data size, slowing search.

3. Final Answer:

   Limit model depth and number of layers to a smaller range -> Option A

4. Quick Check:

   Smaller search space = fewer model options [OK]

Hint: Shrink search space by limiting model complexity [OK]

Common Mistakes:

• Thinking more training epochs speed up search
• Choosing slower optimizers to improve speed
• Using full dataset always speeds search