An AI image classification project teaches a computer to sort pictures into categories, such as recycling types, plant species, or hand gestures. This matters because image classifiers are used in phones, medical tools, security systems, and environmental monitoring. In a school project, the goal is not to build a perfect AI system, but to understand how data, training, testing, and evaluation work together.
A clear workflow helps you make better decisions and explain your results with evidence.
Key Facts
- A typical workflow is collect labeled examples, split data, train the model, test the model, improve the dataset, then deploy.
- A common data split is 80% training data and 20% test data.
- Accuracy = number of correct predictions / total number of predictions.
- Error rate = number of incorrect predictions / total number of predictions.
- A confusion matrix shows correct and incorrect predictions for each class.
- Better labels, balanced classes, and varied lighting usually improve model performance more than simply adding random images.
Vocabulary
- Image classifier
- An image classifier is an AI model that predicts which category an image belongs to.
- Label
- A label is the correct category name attached to an example image, such as cat, dog, paper, or plastic.
- Training data
- Training data is the set of labeled examples the model uses to learn patterns.
- Test data
- Test data is a separate set of labeled examples used to check how well the trained model works on images it has not learned from.
- Confusion matrix
- A confusion matrix is a table that compares the model's predicted labels with the true labels for each class.
Common Mistakes to Avoid
- Using the same images for training and testing is wrong because it can make the model look accurate even when it has only memorized examples.
- Collecting many images for one class and very few for another is wrong because the model may become biased toward the larger class.
- Ignoring bad or unclear labels is wrong because a model learns from the labels you provide, including mistakes.
- Testing only in the same lighting and background as the training images is wrong because the model may fail when real-world conditions change.
Practice Questions
- 1 A student collects 250 labeled images for a classifier and uses an 80% training and 20% test split. How many images go into the training set and how many go into the test set?
- 2 A classifier tests 120 images and predicts 96 correctly. What is its accuracy and error rate?
- 3 A recycling classifier works well in the classroom but fails near a window and outside. Explain what this suggests about the training data and how the student could improve the project.