Alternatives to ResNet-18

ResNet-18 is a variant of the Residual Network (ResNet) family, characterized by its 18 layers and the use of skip connections to mitigate the vanishing gradient problem. It is widely used as a backbone for image classification, object detection, and segmentation tasks, and serves as a common benchmark for new architectures.

At a glance

Executive summary

ResNet-18 is a foundational convolutional neural network (CNN) architecture known for its residual connections, which enable training of very deep networks. It remains a strong baseline for many computer vision tasks, offering a good balance of performance and computational efficiency.

TL;DR

If you need a well-established, efficient CNN for general vision tasks, use ResNet-18; if you need state-of-the-art performance on large datasets and can afford more computational resources, consider Vision Transformers.

Key points

Consider computational resources: ResNet-18 is generally more efficient than Vision Transformers.
Evaluate dataset size: Vision Transformers often excel with very large datasets, while ResNet-18 performs well across a range of sizes.
Assess task complexity: For highly complex tasks or those requiring global context, Vision Transformers might offer an advantage.
Leverage existing pre-trained models: Both architectures have strong pre-trained models, but ResNet-18's are more widely adopted for transfer learning in many scenarios.
Prioritize interpretability: While both can be challenging, CNNs like ResNet-18 often have more intuitive feature maps for understanding learned representations.

Our Take

### Our Take In the realm of deep learning for computer vision, ResNet-18 and Vision Transformers (ViTs) represent two distinct paradigms that have garnered significant attention. ResNet-18, introduced by He et al. in 2015, employs residual connections to facilitate the training of deep networks, achieving impressive results on tasks like image classification and object detection. Its architecture is built on convolutional layers, which have long been the backbone of image processing tasks, allowing it to effectively capture local patterns and spatial hierarchies. On the other hand, Vision Transformers, first proposed by Dosovitskiy et al. in 2020, shift the focus from convolutions to self-attention mechanisms. This allows ViTs to model global relationships within the image data more effectively. Studies have shown that ViTs can outperform traditional convolutional networks when trained on large datasets, as they leverage the power of attention to capture long-range dependencies. However, ResNet-18 still holds its ground, particularly in scenarios with limited data. The architecture's reliance on local features makes it more data-efficient compared to ViTs, which often require extensive datasets to generalize well. For instance, a comparative study by Touvron et al. (2021) highlights that while ViTs excel in large-scale settings, ResNet-18 remains competitive in smaller datasets, showcasing its robustness and efficiency. In conclusion, the choice between ResNet-18 and Vision Transformers largely depends on the specific application and available data. For tasks with ample data and a need for capturing complex relationships, ViTs are the preferred option. Conversely, for smaller datasets or when computational resources are limited, ResNet-18 continues to be a reliable and effective choice.

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

Alternative

Difference

Papers (with ResNet-18)

Avg viability

Vision Transformers

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