To get started in computer vision, first understand the basics of image processing and machine learning. Familiarize yourself with libraries like OpenCV for foundational tasks such as edge detection, filtering, and object tracking.
Progress to deep learning frameworks like TensorFlow or PyTorch to implement advanced models. Start with pre-trained models for tasks like image classification or object detection (e.g., YOLO or ResNet) to gain practical experience.
Use resources like online tutorials, courses (e.g., Stanford’s CS231n), and datasets (e.g., COCO, ImageNet) to enhance your learning. Working on small projects and participating in challenges like Kaggle will help you apply and deepen your knowledge.