Training an image classifier in Computer Vision - Model Pipeline Trace

Model Pipeline - Training an image classifier

This pipeline trains a model to recognize images by learning from labeled pictures. It starts with raw images, processes them, trains a neural network, and then predicts the image category.

Data Flow - 6 Stages

1Data Loading

1000 images x 64x64 pixels x 3 color channels→Load raw images and labels from dataset→1000 images x 64x64 pixels x 3 color channels

Image of a cat labeled as 'cat'

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2Preprocessing

1000 images x 64x64 pixels x 3 color channels→Normalize pixel values to range 0-1→1000 images x 64x64 pixels x 3 color channels

Pixel values changed from 0-255 to 0.0-1.0

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3Train/Test Split

1000 images x 64x64 pixels x 3 color channels→Split dataset into 800 training and 200 testing images→Training: 800 images x 64x64 pixels x 3 channels; Testing: 200 images x 64x64 pixels x 3 channels

800 cat and dog images for training, 200 for testing

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4Feature Engineering

800 images x 64x64 pixels x 3 color channels→Apply data augmentation (flip, rotate) to increase data variety→800 images x 64x64 pixels x 3 color channels (augmented)

Flipped image of a dog to create new training sample

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5Model Training

800 images x 64x64 pixels x 3 color channels→Train convolutional neural network to classify images→Trained model with learned weights

Model learns to recognize features like edges and shapes

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6Evaluation

200 images x 64x64 pixels x 3 color channels→Test model on unseen images to measure accuracy→Accuracy score and loss value

Model predicts 'cat' correctly on 180 out of 200 images

Training Trace - Epoch by Epoch

Epoch 1: ************ (loss=1.2)
Epoch 2: *********    (loss=0.9)
Epoch 3: *******      (loss=0.7)
Epoch 4: *****        (loss=0.5)
Epoch 5: ****         (loss=0.4)

Epoch	Loss ↓	Accuracy ↑	Observation
1	1.2	0.55	Model starts learning basic patterns
2	0.9	0.68	Accuracy improves as model adjusts weights
3	0.7	0.75	Model captures more complex features
4	0.5	0.82	Loss decreases steadily, accuracy rises
5	0.4	0.87	Model converges with good accuracy

Prediction Trace - 5 Layers

Layer 1: Input Layer

Layer 2: Convolutional Layer 1

Layer 3: Pooling Layer

Layer 4: Fully Connected Layer

Layer 5: Output Layer with Softmax

Model Quiz - 3 Questions

What happens to the image pixels during preprocessing?

A Pixels are scaled to values between 0 and 1

B Pixels are converted to black and white

C Pixels are increased to values between 0 and 255

D Pixels are removed from the image

Key Insight

Training an image classifier involves transforming raw images into normalized data, learning features through layers, and improving accuracy by reducing loss over epochs. The softmax output gives clear probabilities for each class, helping the model make confident predictions.