What are the best practices in computer vision?

What are the best practices in computer vision?

Some best practices include data augmentation, using pre-trained models, and ensuring proper data labeling.

Can you explain data augmentation?

Data augmentation involves creating variations of your training data to improve model robustness, like rotating or flipping images.

Why is using pre-trained models beneficial?

Pre-trained models save time and resources, as they leverage learned features from large datasets, improving accuracy.

What should I consider for data labeling?

Ensure consistency, accuracy, and use clear guidelines to avoid bias in your labeled data.

Any tools you recommend for computer vision projects?

Popular tools include OpenCV, TensorFlow, and PyTorch, which offer extensive libraries for computer vision tasks.