Alternatives to Vision Transformers

Vision Transformers apply the self-attention mechanism of Transformers to image recognition by dividing an image into fixed-size patches and processing them as a sequence. This approach allows them to model global context and relationships between different parts of an image, leading to impressive performance on various vision benchmarks.

At a glance

Executive summary

Vision Transformers (ViTs) are a class of neural network architectures that adapt the Transformer model, originally designed for natural language processing, to computer vision tasks. They treat images as sequences of patches, enabling them to capture global relationships within an image more effectively than traditional convolutional neural networks.

TL;DR

If you need state-of-the-art performance on large datasets and have significant computational resources, use Vision Transformers; if you need a well-established, efficient, and robust baseline for general image classification, use ResNet-18.

Key points

Consider Vision Transformers for tasks requiring strong global feature understanding and when large datasets are available for training.
Opt for ResNet-18 when computational resources are limited or for rapid prototyping and baseline establishment.
Evaluate Vision Transformers if you are aiming for the absolute highest accuracy, especially on complex visual recognition problems.
Choose ResNet-18 for applications where model size and inference speed are critical constraints.
If your problem benefits from learning long-range dependencies across image regions, Vision Transformers are a strong candidate.

Our Take

### Our Take In the realm of computer vision, the emergence of Vision Transformers (ViTs) has sparked considerable debate regarding their efficacy compared to traditional convolutional neural networks (CNNs) like ResNet-18. Both architectures have their strengths and weaknesses, and understanding these can guide practitioners in selecting the right model for their tasks. ResNet-18, a pioneering CNN architecture, employs residual connections to facilitate the training of deep networks. It has demonstrated remarkable performance on various image classification benchmarks, achieving a top-1 accuracy of around 71.3% on ImageNet (He et al., 2016). Its design is optimized for local feature extraction, making it particularly effective for tasks where spatial hierarchies are crucial. On the other hand, Vision Transformers, introduced by Dosovitskiy et al. (2020), leverage self-attention mechanisms to capture global dependencies within images. ViTs have shown superior performance, surpassing ResNet-18 in several benchmarks, achieving a top-1 accuracy of up to 88.55% on ImageNet when trained with sufficient data. This performance leap can be attributed to their ability to model long-range interactions, which is often a limitation in CNNs. However, ViTs require significantly more data and computational resources to train effectively. While ResNet-18 can perform well with limited datasets due to its inductive biases, ViTs may struggle in such scenarios, leading to overfitting. In conclusion, the choice between Vision Transformers and ResNet-18 hinges on the specific requirements of the task at hand. For applications with ample data and a need for capturing intricate relationships, ViTs may be the superior choice. Conversely, for scenarios with constrained resources, ResNet-18 remains a robust and reliable option.

Alternative	Difference	Papers (with Vision Transformers)	Avg viability
ResNet-18	—	1	—

Alternative

Difference

Papers (with Vision Transformers)

Avg viability

ResNet-18

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