Model comparison in Computer Vision

Introduction

Model comparison helps us find which model works best for our task by checking their results side by side.

When you have two or more models and want to pick the best one for recognizing images.

When you want to see if a new model improves accuracy compared to an old one.

When you want to check how fast different models run on your device.

When you want to balance between model size and performance for deployment.

When you want to understand which model makes fewer mistakes on your data.

Syntax

Computer Vision

1. Train or load multiple models.
2. Evaluate each model on the same test data.
3. Compare metrics like accuracy, loss, or speed.
4. Choose the model with the best balance for your needs.

Use the same test data for fair comparison.

Metrics can include accuracy, precision, recall, or inference time.

Examples

Compare accuracy scores of two models on the same test set.

Computer Vision

# Evaluate two models on test data
acc_model1 = model1.evaluate(test_images, test_labels, verbose=0)[1]
acc_model2 = model2.evaluate(test_images, test_labels, verbose=0)[1]
print(f"Model1 accuracy: {acc_model1}")
print(f"Model2 accuracy: {acc_model2}")

Measure how fast each model makes predictions on a few images.

Computer Vision

# Compare inference time
import time
start = time.time()
model1.predict(test_images[:10])
print(f"Model1 time: {time.time() - start} seconds")
start = time.time()
model2.predict(test_images[:10])
print(f"Model2 time: {time.time() - start} seconds")

Sample Model

This code trains two different CNN models on the MNIST dataset and compares their accuracy on the test set.

Computer Vision

import tensorflow as tf
from tensorflow.keras import layers, models

# Load sample data
(train_images, train_labels), (test_images, test_labels) = tf.keras.datasets.mnist.load_data()
train_images = train_images / 255.0
test_images = test_images / 255.0

# Model 1: Simple CNN
model1 = models.Sequential([
    layers.Reshape((28,28,1), input_shape=(28,28)),
    layers.Conv2D(16, 3, activation='relu'),
    layers.MaxPooling2D(),
    layers.Flatten(),
    layers.Dense(10, activation='softmax')
])
model1.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
model1.fit(train_images, train_labels, epochs=2, verbose=0)

# Model 2: Deeper CNN
model2 = models.Sequential([
    layers.Reshape((28,28,1), input_shape=(28,28)),
    layers.Conv2D(32, 3, activation='relu'),
    layers.MaxPooling2D(),
    layers.Conv2D(64, 3, activation='relu'),
    layers.MaxPooling2D(),
    layers.Flatten(),
    layers.Dense(10, activation='softmax')
])
model2.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
model2.fit(train_images, train_labels, epochs=2, verbose=0)

# Evaluate both models
loss1, acc1 = model1.evaluate(test_images, test_labels, verbose=0)
loss2, acc2 = model2.evaluate(test_images, test_labels, verbose=0)

print(f"Model 1 accuracy: {acc1:.4f}")
print(f"Model 2 accuracy: {acc2:.4f}")

Important Notes

Training for more epochs usually improves accuracy but takes longer.

A deeper model may perform better but needs more computing power.

Always use the same test data to compare models fairly.

Summary

Model comparison helps pick the best model by checking their results on the same data.

Compare models using accuracy, loss, or speed depending on your goal.

Simple code can train and evaluate multiple models to see which works best.