What to know before you read.
- Computer vision includes classification, detection and segmentation—each produces different outputs.
- Image leakage and unrepresentative conditions can make evaluation look stronger than reality.
- Transfer learning is the most practical route to a first complete project.
Choose the visual question
Image classification assigns a label to a complete image. Object detection locates and labels objects. Segmentation assigns labels at pixel or region level. OCR extracts text, while tracking follows objects over video. Defining the output determines the data annotation, model and evaluation you need.
Images are data with context
Resolution, lighting, camera angle, background and capture device can all become unintended signals. Review class balance and near-duplicates. Keep images from the same person, scene or recording session in one split to prevent leakage.
Use transfer learning deliberately
Start with a pretrained model and adapt its final layers to a small dataset. Compare against a simple baseline and record training curves. Additional architecture is not progress unless it improves the relevant error pattern without unacceptable cost.
Evaluate failure conditions
Measure class-level precision and recall, then inspect confusion examples. Test lighting, occlusion, image quality and groups relevant to the application. For consequential use, document human review and what the system must never decide alone.
Build one bounded application
Create an image classifier with a capture guide, data statement, model comparison, error gallery and simple interface. The Computer Vision Certificate of Specialisation connects image fundamentals, deep-learning workflows and a capstone in 30 Hours over 2 Months.
Questions readers often ask.
Do I need deep learning for computer vision?+
Modern vision frequently uses deep learning, though classical image-processing techniques remain useful for defined operations.
How much image data do I need?+
It depends on task variation and model choice. Transfer learning can work with smaller datasets, but representative coverage still matters.
What computer-vision project is beginner friendly?+
A small image classifier with careful data splits, failure analysis and a usable interface is a strong start.
Project guide