Model optimization (pruning, quantization) in Computer Vision - Model Pipeline Trace

Model Pipeline - Model optimization (pruning, quantization)

This pipeline shows how a computer vision model is made smaller and faster using pruning and quantization. These steps help the model run well on devices like phones without losing much accuracy.

Data Flow - 7 Stages

1Original Data Input

1000 images x 64 x 64 pixels x 3 color channels→Raw images loaded for training→1000 images x 64 x 64 x 3

Image of a cat with RGB pixel values

↓

2Preprocessing

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

Pixel value 128 becomes 0.5

↓

3Feature Extraction

1000 images x 64 x 64 x 3→Convolutional layers extract features→1000 images x 16 x 16 x 32 feature maps

Edges and shapes detected in images

↓

4Model Training

1000 images x 16 x 16 x 32→Train CNN classifier→Trained model weights

Weights learned to recognize cats and dogs

↓

5Pruning

Trained model weights→Remove 30% of smallest weights (set to zero)→Smaller model with sparse weights

Weights near zero removed to reduce size

↓

6Quantization

Pruned model weights (float32)→Convert weights from float32 to int8→Smaller model weights in int8 format

Weight 0.123 float32 becomes 12 int8

↓

7Optimized Model

Quantized model weights→Model ready for fast inference on device→Compressed model with reduced size

Model size reduced from 20MB to 7MB

Training Trace - Epoch by Epoch

Epochs
1 |***************
3 |********************
5 |*************************
7 |******************************
10|********************************
Loss
1.2|***************
0.8|************
0.5|*******
0.4|*****
0.35|****

Epoch	Loss ↓	Accuracy ↑	Observation
1	1.2	0.55	Model starts learning basic features
3	0.8	0.70	Accuracy improves as model learns patterns
5	0.5	0.82	Model converging with good accuracy
7	0.4	0.87	Loss decreases steadily, accuracy rises
10	0.35	0.90	Training stabilizes with high accuracy

Prediction Trace - 5 Layers

Layer 1: Input Image

Layer 2: Convolutional Layer

Layer 3: Pruned Weights Applied

Layer 4: Quantized Weights Applied

Layer 5: Output Layer (Softmax)

Model Quiz - 3 Questions

What is the main purpose of pruning in this model pipeline?

A Increase the number of model layers

B Remove less important weights to reduce model size

C Convert weights to integers

D Normalize input images

Key Insight

Pruning and quantization help make computer vision models smaller and faster with little loss in accuracy. This allows models to run efficiently on devices with limited resources.