Abhishek k
6 min readMay 17, 2023

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Image Classification using Convolutional Neural Network and Vision Transformer

I worked on a research project that sought to provide a comprehensive analysis of how convolutional neural networks (CNNs) and vision transformers compare and contrast. Thanks to this research, I now have a thorough appreciation for the advantages and disadvantages of each of these designs. In order to provide a complete picture, I linked to the datasets I used in my research and gave a full explanation of the project on GitHub.

Introduction

Computer vision relies heavily on image categorization for tasks as diverse as autonomous vehicle navigation and medical diagnosis. Several deep learning models have been created throughout the years to meet this difficulty. Convolutional Neural Networks (CNNs) and Vision Transformers have emerged as two of the most popular designs in recent years. In this piece, we compare the two methods and talk about how combining them might enhance picture categorization.

CNN FOR CLASSIFICATION

Understanding Convolutional Neural Networks (CNNs)

Since the ground-breaking work of LeNet-5 and AlexNet, CNNs have been at the forefront of image classification challenges. These networks were developed with the express purpose of identifying spatial hierarchies and local patterns in pictures. To do this, they use convolutional layers to focus in on local features and pooling layers to downsample the feature maps while retaining crucial details despite the reduction in spatial dimensions.

In applications such as object identification, CNNs shine because of their superior ability to capture local patterns, edges, and textures. However, classic CNNs struggle to scale with increasing picture size and dataset complexity because they use receptive fields of fixed size and have restricted attention methods.

VISION TRANSFORMER FOR CLASSIFICATION

Vision Transformers

Vision However, transformers have recently attracted interest due to their capacity to record semantic information and long-range relationships in pictures. In their original form, transformers showed exceptional performance in natural language processing tasks. To do this, the convolutional layers in the transformer architecture are removed and replaced with self-attention techniques.

Images can be processed efficiently by Vision Transformers thanks to their use of self-attention rather than explicit spatial hierarchies. This adaptability allows them to focus on important areas, despite their physical distance from one another, and to capture correlations between far-flung pixels. Thus, Vision Transformers have excelled in picture captioning and image production, two activities that require a comprehensive grasp of an image’s content.

The use of Convolutional Neural Networks with Vision Transformers

Researchers have looked at hybrid models that mix CNNs and Vision Transformers to take use of both architectures. Using a CNN as its foundation, the model may then make use of Vision Transformers’ attention processes to better grasp its global environment. With this combination, we obtain the speed with which CNNs analyze local features and the accuracy with which Vision Transformers represent global dependencies.

The hybrid vision transformer (HVT) is one example of a common hybrid design. It uses a convolutional neural network (CNN) as its foundation, like ResNet or EfficientNet, and then adds a Vision Transformer component. In order to capture global context and dependencies, the Vision Transformer module analyzes the feature maps generated by the CNN backbone. Together, they have achieved state-of-the-art performance on a number of picture categorization benchmarks.

In contrast to Vision Transformers, how do CNNs perform?

Both convolutional neural networks (CNNs) and visual transformers (transformers) are deep learning models useful for this task. However, the two models do not coincide in every respect.

In general, convolutional neural networks (CNNs) excel at learning local features, whereas vision transformers excel at learning global features. This is because CNNs employ convolutional layers to learn characteristics that are unique to small parts of the input picture. In contrast, vision transformers learn characteristics that are more global in scope by applying self-attention layers to the input picture.

CNNs are more effective than vision transformers in most cases. The reason for this is that, unlike vision transformers, CNNs may learn features in a hierarchical fashion.

Both Pros and Cons

There are a number of benefits to combining CNNs with vision transformers. These hybrid models outperform their single-architecture counterparts in picture classification tasks because they are able to collect both local and global information. While the CNN core ensures stability across regional differences and particulars, the Vision Transformer module records overarching meanings and associations.

However, problems arise when various designs are combined, including increased computing complexity and the need for fine-tuning of parameters. Careful architectural design and training methodologies are needed to integrate CNNs with vision transformers for optimal information flow and learning performance. Vision transformers include a lot of parameters, which may make training and inference time-consuming and resource-intensive, calling for dedicated hardware and optimization strategies.

Conclusion

Convolutional neural networks (CNNs) and vision transformers together provide a potent solution to the important challenge of image categorization in computer vision. State-of-the-art performance on image classification benchmarks is achieved by hybrid models, which combine the local feature extraction power of CNNs with the global context modeling of Vision Transformers.

A convolutional neural network (CNN) might be the superior option if the job calls for the model to learn regional information. A vision transformer might be the superior option if the job calls for the model to learn globally applicable characteristics.

Researchers are still digging into these designs, so more developments are to be expected as time goes on. Developments and innovations in the fields of image analysis and interpretation.

One example of the ongoing development in deep learning and computer vision is the combination of CNNs and vision transformers. Exciting breakthroughs that expand the capabilities of picture categorization and associated tasks are on the horizon as the discipline develops.

GITHUB :

https://github.com/BitwiseData/Deep-Learning

REFERENCES :

[1] D. H. Hubel and T. N. Wiesel, “Receptive fields and functional architecture of monkey striate cortex,” J. Physiol., vol. 195, no. 1, 1968, doi: 10.1113/jphysiol.1968.sp008455.

[2] Y. LeCun et al., “Backpropagation Applied to Handwritten Zip Code Recognition,” Neural Comput., vol. 1, no. 4, 1989, doi: 10.1162/neco.1989.1.4.541.

[3] A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” in Advances in Neural Information Processing Systems, 2012, vol. 2.

[4] O. Russakovsky et al., “ImageNet Large Scale Visual Recognition Challenge,” Int. J. Comput. Vis., vol. 115, no. 3, 2015, doi: 10.1007/s11263–015–0816-y.

[5] M. D. Zeiler and R. Fergus, “Visualizing and understanding convolutional networks,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2014, vol. 8689 LNCS, no. PART 1, doi: 10.1007/978–3–319–10590–1_53.

[6] M. Lin, Q. Chen, and S. Yan, “Network in network,” 2nd Int. Conf. Learn. Represent. ICLR 2014 — Conf. Track Proc., pp. 1–10, 2014.

[7] A. Vaswani et al., “Attention is all you need,” in Advances in Neural Information Processing Systems, 2017, vol. 2017- December.

[8] M. Chen et al., “Generative pretraining from pixels,” in 37th International Conference on Machine Learning, ICML 2020, 2020, vol. PartF168147–3.

[9] M. Is, R. For, and E. At, “An image is worth 16x16 words,” Int. Conf. Learn. Represent., 2021.

[10] N. Carion, F. Massa, G. Synnaeve, N. Usunier, A. Kirillov, and S. Zagoruyko, “End-to-End Object Detection with Transformers,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2020, vol. 12346 LNCS, doi: 10.1007/978–3–030–58452–8_13.

[11] S. Zheng et al., “Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers,” 2021, doi: 10.1109/CVPR46437.2021.00681.

[12] H. Chen et al., “Pre-trained image processing transformer,” 2021, doi: 10.1109/CVPR46437.2021.01212.

[13] L. Zhou, Y. Zhou, J. J. Corso, R. Socher, and C. Xiong, “End-to-End Dense Video Captioning with Masked Transformer,” 2018, doi: 10.1109/CVPR.2018.00911

[14] https://towardsdatascience.com/using-transformers-for-computer-vision-6f764c5a078b

[15] https://developersbreach.com/convolution-neural-network-deep-learning/

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