CNN architecture review in Computer Vision - Model Pipeline Trace

Model Pipeline - CNN architecture review

This pipeline shows how a Convolutional Neural Network (CNN) learns to recognize images by processing raw pictures, extracting features, training on those features, and then making predictions.

Data Flow - 8 Stages

1Input Images

1000 images x 64 x 64 x 3→Raw color images of size 64x64 pixels with 3 color channels (RGB)→1000 images x 64 x 64 x 3

An image of a cat represented as a 64x64 grid with red, green, blue values

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2Convolutional Layer 1

1000 images x 64 x 64 x 3→Apply 32 filters of size 3x3 with ReLU activation→1000 images x 62 x 62 x 32

Filters detect edges and simple shapes in the image

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3Max Pooling Layer 1

1000 images x 62 x 62 x 32→Downsample by 2x2 max pooling→1000 images x 31 x 31 x 32

Reduce image size while keeping important features

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4Convolutional Layer 2

1000 images x 31 x 31 x 32→Apply 64 filters of size 3x3 with ReLU activation→1000 images x 29 x 29 x 64

Filters detect more complex shapes and textures

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5Max Pooling Layer 2

1000 images x 29 x 29 x 64→Downsample by 2x2 max pooling→1000 images x 14 x 14 x 64

Further reduce size, keep strongest features

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6Flatten Layer

1000 images x 14 x 14 x 64→Flatten 3D feature maps into 1D vectors→1000 images x 12544

Convert features into a long list for dense layers

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7Dense Layer

1000 images x 12544→Fully connected layer with 128 neurons and ReLU→1000 images x 128

Combine features to learn complex patterns

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8Output Layer

1000 images x 128→Fully connected layer with 10 neurons and softmax→1000 images x 10

Predict probabilities for 10 classes (e.g., digits 0-9)

Training Trace - Epoch by Epoch

Loss
2.0 |****
1.5 |*** 
1.0 |**  
0.5 |*   
0.0 +----
     1 2 3 4 5 Epochs

Epoch	Loss ↓	Accuracy ↑	Observation
1	1.85	0.35	Model starts learning, accuracy low, loss high
2	1.20	0.55	Loss decreases, accuracy improves
3	0.85	0.70	Model learns important features
4	0.60	0.80	Good progress, model getting better
5	0.45	0.85	Loss low, accuracy high, training converging

Prediction Trace - 8 Layers

Layer 1: Input Image

Layer 2: Convolutional Layer 1

Layer 3: Max Pooling Layer 1

Layer 4: Convolutional Layer 2

Layer 5: Max Pooling Layer 2

Layer 6: Flatten Layer

Layer 7: Dense Layer

Layer 8: Output Layer

Model Quiz - 3 Questions

What does the first convolutional layer mainly detect?

A Edges and simple shapes

B Final class probabilities

C Flattened feature vectors

D Downsampled images

Key Insight

This CNN architecture shows how images are transformed step-by-step from raw pixels to class probabilities. Convolutional layers extract features, pooling layers reduce size while keeping important info, and dense layers learn complex patterns. Training improves accuracy by reducing loss steadily.