Build Your First Computer Vision Project: A Step-by-Step Guide

A working computer vision demo on your laptop is the single most impressive portfolio piece for a fresher applying to AI roles. Here is how to build one in a weekend without drowning in theory.

Pick a small, real problem

Forget face recognition or self-driving cars for v1. Pick something narrow: classify Indian currency notes by denomination, detect helmets on two-wheelers, count people in a queue. Narrow problems converge fast and demo cleanly.

The five steps

• Collect — 200-500 images. Phone camera works fine. Diverse lighting matters more than count.
• Label — use a free tool like LabelImg or Roboflow. Budget 2-3 hours.
• Train — fine-tune a pretrained model (YOLOv8 or MobileNet) on Google Colab. Free GPU is enough.
• Evaluate — hold out 20% of data, report precision and recall, look at where it fails.
• Demo — wrap it in a Streamlit or Gradio app and deploy to Hugging Face Spaces. Free.

Common mistakes

Most beginners train too long, overfit, then claim 99% accuracy on the training set. Always report test-set metrics. Also include a few failure examples in your README — recruiters trust honest projects more than perfect ones.

Take it further

Once v1 works, add real-time inference from a webcam, deploy to a phone with TensorFlow Lite, or extend the dataset and retrain. Each step is a paragraph on your resume.