Computer vision enhances AI model training by improving data quality and providing meaningful visual insights. Preprocessing images through techniques like resizing, normalization, and augmentation (e.g., rotation, flipping) ensures robust training.
Using annotated datasets, such as COCO or Pascal VOC, allows models to learn effectively from labeled data. Tools like OpenCV or LabelImg can assist in preparing high-quality annotations.
Transfer learning with pre-trained models like ResNet or EfficientNet speeds up training and improves accuracy, especially when working with limited datasets. Incorporating vision techniques in AI pipelines ensures better performance in tasks like object detection and classification.