CNN vs Vision Transformer: Which Architecture Should You Choose for Computer Vision?

The debate between convolutional neural networks (CNNs) and Vision Transformers (ViTs) continues to shape computer vision. CNNs have been the backbone for years, excelling in local feature extraction and efficiency on smaller datasets. ViTs, inspired by NLP transformers, capture global dependencies but require more data and compute. Recent hybrid models aim to combine the best of both. This article explores key differences: inductive biases, scalability, and real-world performance. For tasks like image classification and object detection, ViTs often outperform CNNs on large datasets, while CNNs remain competitive for edge deployment. Understanding these trade-offs helps engineers choose the right architecture for their projects.