CNNs have drawbacks such as lengthy training time, the need for large labeled data sets and susceptibility to overfitting. Network complexity can also affect performance. However, CNNs remain a widely used and effective tool in computer vision, including object detection and segmentation, despite limitations in tasks requiring contextual knowledge like NLP.
Convolutional neural networks have several drawbacks that can make using them in some machine learning applications difficult. For instance, CNN training can take a while, especially for large data sets, because CNNs are computationally expensive. Furthermore, creating the CNN architecture can be challenging and necessitates a thorough comprehension of the fundamental ideas of artificial neural networks.