To learn computer vision, start with foundational topics like image processing and basic machine learning. Use OpenCV for tasks like edge detection, thresholding, and contour analysis to build practical skills.
Gradually explore deep learning-based techniques using frameworks like TensorFlow or PyTorch. Focus on convolutional neural networks (CNNs) and their applications in object detection, segmentation, and classification. Experiment with pre-trained models like YOLO, ResNet, or MobileNet for hands-on experience.
Utilize resources like Stanford’s CS231n, Coursera courses, and tutorials on YouTube. Working on projects and participating in competitions like Kaggle can deepen your understanding and provide practical exposure.