CV project workflow in Computer Vision - Model Pipeline Trace

Model Pipeline - CV project workflow

This workflow shows how a computer vision project processes images from start to finish. It begins with collecting images, then prepares them, extracts important features, trains a model to recognize patterns, checks how well it learns, and finally uses the model to make predictions on new images.

Data Flow - 6 Stages

1Image Collection

0 images→Gather images from cameras or datasets→1000 images x 256x256 pixels x 3 color channels

Photos of cats and dogs collected from online sources

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2Preprocessing

1000 images x 256x256x3→Resize images to 128x128, normalize pixel values to 0-1→1000 images x 128x128 pixels x 3 channels

Original 256x256 images resized and pixel values scaled

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3Feature Engineering

1000 images x 128x128x3→Apply convolutional filters to extract edges and shapes→1000 images x 124x124 pixels x 16 feature maps

Edges and textures highlighted by filters in feature maps

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4Model Training

800 images x 124x124x16 (train set)→Train CNN model to classify images into categories→Trained model with learned weights

Model learns to distinguish cats vs dogs from training images

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5Model Evaluation

200 images x 124x124x16 (test set)→Calculate accuracy and loss on unseen images→Accuracy: 0.85, Loss: 0.35

Model correctly classifies 85% of test images

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6Prediction

New image x 128x128x3→Preprocess and feed image to trained model for prediction→Predicted class label (e.g., 'cat')

Model predicts a new photo is a cat with 90% confidence

Training Trace - Epoch by Epoch

Loss
1.2 |*       
0.8 | **     
0.6 |  ***   
0.45|    ****
0.35|     *****
     ----------------
      1  2  3  4  5  Epochs

Epoch	Loss ↓	Accuracy ↑	Observation
1	1.2	0.45	Model starts learning, loss high, accuracy low
2	0.8	0.65	Loss decreases, accuracy improves
3	0.6	0.75	Model learns important features
4	0.45	0.82	Good progress, model generalizes better
5	0.35	0.85	Training converges, accuracy stabilizes

Prediction Trace - 5 Layers

Layer 1: Input Image

Layer 2: Convolutional Layer

Layer 3: Pooling Layer

Layer 4: Fully Connected Layer

Layer 5: Softmax Activation

Model Quiz - 3 Questions

What happens to the image size during preprocessing?

A It is resized smaller to 128x128 pixels

B It is enlarged to 512x512 pixels

C It stays the same size

D It is converted to grayscale

Key Insight

This visualization shows how images are prepared and transformed step-by-step in a computer vision project. The model learns by reducing loss and increasing accuracy over time. The final prediction gives probabilities for each class, helping us understand the model's confidence.