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.
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Architecture search concepts in Computer Vision - Model Pipeline Trace
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
Test your understanding
What is the main goal of architecture search in this pipeline?
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.