CV project workflow in Computer Vision

Introduction

We follow a clear step-by-step plan to build computer vision projects so they work well and solve real problems.

You want to build an app that recognizes objects in photos.

You need to sort images into categories automatically.

You want to detect faces or features in pictures.

You plan to improve a robot's vision to understand its environment.

You want to analyze medical images to help doctors.

Syntax

Computer Vision

1. Define the problem
2. Collect and prepare data
3. Choose or build a model
4. Train the model
5. Evaluate the model
6. Improve and tune the model
7. Deploy the model
8. Monitor and maintain

This is a general workflow, steps may repeat or overlap.

Good data and clear goals make the project easier and better.

Examples

This example shows a simple cat detector project following the workflow.

Computer Vision

1. Problem: Detect cats in photos
2. Data: Gather cat and no-cat images
3. Model: Use a simple CNN
4. Train: Teach model on images
5. Evaluate: Check accuracy
6. Improve: Add more data or layers
7. Deploy: Put model in a phone app
8. Monitor: Watch app performance

This example uses a pretrained model and fine-tuning for fruit sorting.

Computer Vision

1. Problem: Sort fruits by type
2. Data: Collect fruit pictures
3. Model: Use pretrained model like ResNet
4. Train: Fine-tune on fruit data
5. Evaluate: Measure precision and recall
6. Improve: Adjust learning rate
7. Deploy: Use in supermarket scanner
8. Monitor: Update model with new fruits

Sample Model

This program follows the CV project workflow to build a digit classifier using MNIST data.

Computer Vision

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

# 1. Define problem: classify digits from images
# 2. Load data
(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()

# Normalize data
x_train, x_test = x_train / 255.0, x_test / 255.0

# 3. Build model
model = models.Sequential([
    layers.Flatten(input_shape=(28, 28)),
    layers.Dense(128, activation='relu'),
    layers.Dense(10, activation='softmax')
])

# 4. Compile model
model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])

# 5. Train model
history = model.fit(x_train, y_train, epochs=3, validation_split=0.1, verbose=2)

# 6. Evaluate model
loss, accuracy = model.evaluate(x_test, y_test, verbose=0)
print(f'Test accuracy: {accuracy:.4f}')

Important Notes

Good quality and enough data is key for success.

Start simple, then improve your model step by step.

Always check your model on new data to avoid surprises.

Summary

Follow a clear step plan from problem to deployment.

Data preparation and evaluation are very important.

Keep improving and monitoring your model after deployment.

Practice

1. Which step comes first in a typical computer vision project workflow?

easy

A. Monitor model performance

B. Deploy the model to production

C. Tune hyperparameters

D. Define the problem and collect data

Solution

1. Step 1: Understand the project start

   The first step is to clearly define what problem you want to solve and gather the images or videos needed.

2. Step 2: Recognize the order of workflow steps

   Data collection must happen before training, tuning, or deployment.

3. Final Answer:

   Define the problem and collect data -> Option D

4. Quick Check:

   First step = Define problem and collect data [OK]

Hint: Start with problem definition and data collection [OK]

Common Mistakes:

• Thinking deployment is the first step
• Skipping problem definition
• Ignoring data collection importance

2. Which of the following is the correct syntax to split data into training and testing sets in Python using scikit-learn?

easy

A. train_test_split(data, test_size=0.2)

B. split_train_test(data, 0.2)

C. train_test(data, test=0.2)

D. train_test_split(data, test=0.2)

Solution

1. Step 1: Recall scikit-learn function name and parameters

   The correct function is train_test_split with parameter test_size to specify test data fraction.

2. Step 2: Check parameter correctness

   train_test_split(data, test_size=0.2) uses correct function and parameter names.

3. Final Answer:

   train_test_split(data, test_size=0.2) -> Option A

4. Quick Check:

   Correct function and parameter = train_test_split(data, test_size=0.2) [OK]

Hint: Remember exact function and parameter names from scikit-learn [OK]

Common Mistakes:

• Using wrong function name
• Using incorrect parameter names
• Confusing test_size with test

3. Given this code snippet for training a simple CNN model, what will be the printed output after training for 1 epoch?

import tensorflow as tf
model = tf.keras.Sequential([
  tf.keras.layers.Conv2D(16, 3, activation='relu', input_shape=(28,28,1)),
  tf.keras.layers.Flatten(),
  tf.keras.layers.Dense(10, activation='softmax')
])
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
history = model.fit(x_train, y_train, epochs=1, batch_size=32)
print(history.history['accuracy'][0])

medium

A. An error because 'accuracy' is not in history

B. The loss value after training

C. A float value between 0 and 1 representing training accuracy

D. The number of training samples

Solution

1. Step 1: Understand model.fit output

   The history object stores metrics per epoch. Accessing history.history['accuracy'][0] gives training accuracy after first epoch.

2. Step 2: Confirm metric requested

   Since metrics=['accuracy'] was set, accuracy is recorded and printed as a float between 0 and 1.

3. Final Answer:

   A float value between 0 and 1 representing training accuracy -> Option C

4. Quick Check:

   history.history['accuracy'][0] = training accuracy [OK]

Hint: history.history['accuracy'][0] holds first epoch accuracy [OK]

Common Mistakes:

• Confusing accuracy with loss
• Expecting an error accessing accuracy
• Thinking it prints sample count

4. You trained a model but it performs poorly on new images. Which step in the workflow might be causing this issue?

medium

A. Monitoring was set up correctly

B. Data preparation was insufficient or incorrect

C. Hyperparameters were tuned perfectly

D. Model deployment was done too early

Solution

1. Step 1: Analyze poor model performance cause

   Poor results on new data often mean the model did not learn well, usually due to bad or insufficient data preparation.

2. Step 2: Eliminate unrelated options

   Deployment timing, perfect hyperparameters, or monitoring setup do not directly cause poor initial performance.

3. Final Answer:

   Data preparation was insufficient or incorrect -> Option B

4. Quick Check:

   Poor performance = bad data prep [OK]

Hint: Check data prep first when model fails on new data [OK]

Common Mistakes:

• Blaming deployment timing
• Assuming hyperparameters are always perfect
• Ignoring data quality issues

5. In a computer vision project, after deploying your model, you notice accuracy drops over time. What is the best next step to maintain model performance?

hard

A. Collect new data and retrain the model regularly

B. Stop monitoring since model is deployed

C. Reduce the size of the training dataset

D. Ignore the drop as normal and do nothing

Solution

1. Step 1: Understand model drift after deployment

   Models can lose accuracy as data changes. Collecting new data and retraining helps adapt to changes.

2. Step 2: Evaluate other options

   Stopping monitoring or ignoring drops will worsen performance. Reducing training data size is counterproductive.

3. Final Answer:

   Collect new data and retrain the model regularly -> Option A

4. Quick Check:

   Maintain performance = retrain with new data [OK]

Hint: Retrain model regularly with fresh data after deployment [OK]

Common Mistakes:

• Ignoring monitoring after deployment
• Reducing training data size
• Assuming model never needs updates