10.2.1 Image Classification Roadmap: Image In, Label Out

Image classification answers one question: given a whole image, which class best describes it?

See the Classification Loop First

Classification is the simplest vision output, but it still depends on data split, augmentation, architecture, loss, metrics, and error examples.

Run a Prediction Check

This script mimics the last step of a classifier: choose the label with the highest score.

labels = ["cat", "dog", "panda"]
scores = [0.12, 0.74, 0.14]

best_index = max(range(len(scores)), key=lambda index: scores[index])

print("prediction:", labels[best_index])
print("confidence:", scores[best_index])

Expected output:

prediction: dog
confidence: 0.74

In real projects, never show only the top class. Keep confidence, wrong examples, and confusion patterns.

Learn in This Order

Step	Read	Practice Output
1	Data augmentation	Explain which changes preserve the class and which create risk
2	Modern architectures	Compare feature extractor, classifier head, and pretrained backbone
3	Training techniques	Track split, loss, accuracy, overfitting, and error samples

Evidence to Keep

Keep this page’s proof of learning as a small evidence card:

Dataset Split

train/test images, class names, and class balance

Prediction

label, confidence, and at least one misclassified image

Metric

accuracy, F1, confusion matrix, and class-level errors

Failure Check

augmentation changes label meaning, class imbalance, leakage, or overfitting

Expected Output

model result table and saved error examples

Pass Check

You pass this chapter when you can run a minimal classifier, show train/validation metrics, and explain at least one failure image.

Check reasoning and explanation

1. A passing answer maps the task to the right visual output: class label, bounding box, mask, OCR text, embedding, or video event.
2. The evidence should include a rendered visual artifact and one metric or qualitative error note.
3. A good self-check names one visual failure mode such as class confusion, missed objects, bad masks, lighting shift, domain shift, or weak annotation quality.