How Vision Transformers Are Outperforming CNNs in 2025

In 2025, vision transformers (ViTs) will dominate many computer vision use cases. Examples include autonomous driving and medical image analysis. Similarly, ViTs will help accelerate facial recognition, satellite imagery interpretation, and retail video analytics.

However, at the same time, the demand for traditional convolutional neural networks (CNNs) will take a hit. In many use cases, ViTs are now setting new performance benchmarks. So, saying that they outperform CNNs is an evidence-backed statement. This post will explore the current dynamics between vision transformers and CNNs.

What Are Vision Transformers?

Vision Transformers (ViTs) are based on the transformer architecture. This architecture uses self-attention mechanisms, letting the model weigh the importance of every part of an input image. Therefore, it can compare the various visual elements on the image-level instead of only one fixed quadrant. For modern computer vision solutions, this approach ensures a broader context detection capability.

Here is what makes ViTs different from CNNs on the methodology front:

1. ViTs avoid analyzing images through convolutions. Instead, they break images into tiny fixed-size patches.

2. These patches become embedding vectors, which are numerical representations. The embedding vectors will capture visual information.

3. Finally, the model processes these embeddings as if they belong to one complete entity. Therefore, ViTs exhibit a collective or comprehensive understanding of both local and global relationships between visual elements.

It is this global attention approach that puts ViTs many steps ahead of convolutional neural networks. They can see the big picture rather than spending all effort and resources on a few areas within the input image. However, related development and operations (DevOps) costs can be greater than those of convolutional neural networks.

Understanding CNNs and Their Limitations

CNNs use convolutional layers to examine and process small regions of an image. In essence, they capture local patterns. That is why the visual elements, like edges, textures, and shapes, are crucial to these neural networks. Over multiple layers, CNNs learn to recognize more complex features, such as faces or inanimate objects.

However, the other side of the coin is that CNNs struggle to capture long-range dependencies. In simpler terms, they require more guidance on establishing the relationships between distant parts of an image.

By being too focused on local information, CNNs lose their ability to grasp what the complete image will mean. This restriction affects context identification applications that depend on visual input.

Additionally, users will need unique DevOps consulting services because convolutional neural networks rely on carefully crafted architectures. The CNNs also require large labeled datasets for training. These aspects can contribute to the total cost and time of using them.

Digging Deeper: What Are the Key Reasons ViTs Are Outperforming CNNs?

1. Better Contextual Understanding

The self-attention mechanism in ViTs surpasses CNNs in spatial relationship analytics. For example, a photograph might depict a crowded public spot. Such an image has many faces, reflections, shadows, and other texture-related details. However, a vision transformer can more easily find the context. It will highlight people in the crowd and distinguish between other objects.

2. Scalability with Data & Compute

ViTs scale without any issues when users need to handle large datasets, and more powerful computing resources become available for use. Moreover, as the size of the training data gets bigger, vision transformers’ performance rapidly improves. On the other hand, CNNs often fall short after a certain point if users assign the same datasets or computing power to them.

3. Simplified Architecture

Developers cannot be too liberal with CNNs’ architecture. It requires attentive design if development teams want to help users achieve optimal results. Vision transformers, however, rely on a more uniform and flexible design. Simpler is better. That simplicity makes adaptation and tuning across different vision applications more practical as opposed to CNNs.

4. Transfer Learning and Pretraining Advantages

Pretrained ViT models can take on new tasks by using their pretraining advantages. It is how anyone can teach large language models to transfer learning between different problems. The models on their own will make more granular adjustments to complete new requests that will differ from the earlier ones. From the industrial application perspective, that is a major plus point of ViTs over CNNs.

Challenges and Future Outlook

Despite their success, vision transformers demand considerable computational resources. Besides, training them from scratch is an expensive endeavor. As a result, researchers are now developing hybrid models. These models will combine CNNs’ efficiency with ViTs’ global attention capabilities, helping get the best out of both technologies.

As hardware becomes more powerful, handling vast datasets will be more efficient. So, a future where stakeholders can stop relying on CNNs and dedicate more capital to ViTs is not that far. ViTs’ ability to generalize across visual tasks also makes them inspirational for the next generation of AI-driven visual intelligence and associated technology specialists.

Conclusion

Vision transformers have many strengths, making them an ideal choice for computer vision applications. Since they can help overcome the constraints of CNNs, many global context awareness projects will benefit from ViT adoption.

ViTs are also scalable. That is why their popularity among enterprise tech teams is set to rise. In 2025 and beyond, ViTs will keep outperforming CNNs, becoming a core enabler of redefining how machines perceive and interpret the actual, physical world.

I am working as a digital marketing analyst at SG Analytics which is a global data analytics company that provides research and analytics services globally.