Computer vision is a fascinating field of artificial intelligence, and building hands-on projects is one of the best ways to learn. Here are five beginner-friendly projects that will help you get started and create a strong portfolio:
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Image Classification with CIFAR-10 – Train a neural network to recognize objects in 10 categories, such as airplanes, cars, and birds. This classic project introduces you to convolutional neural networks and data augmentation.
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Object Detection with YOLO – Use a pre-trained YOLO (You Only Look Once) model to detect and localize objects in images or video streams. This project teaches you about bounding boxes, non-max suppression, and real-time inference.
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Face Recognition System – Build an app that identifies faces in images using libraries like OpenCV and face_recognition. You'll learn about face detection, encoding, and matching.
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Handwritten Digit Recognition – Implement a model to recognize digits from the MNIST dataset. This is a great project to understand image preprocessing, model training, and evaluation metrics.
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Emotion Detection from Facial Expressions – Create a model that predicts emotions like happiness, sadness, or anger from face images. This project combines computer vision with deep learning and sentiment analysis.
Each of these projects can be expanded with real-world data or deployed as a simple web app to showcase your skills. Start with one and gradually tackle more complex challenges to boost your AI portfolio.