EfficientNet : A New Approach to Model Scaling
Convolutional Neural Networks (CNNs), likewise referred to as ConvNets, are a major category in the field of neural networks and can been employed for tasks such as image recognition and classification. CNNs are utilized extensively in many fields, such as object detection, facial recognition, and others.
But at the same time traditional CNN models face a number of issues like-High Computational Cost,Limited Receptive Field, Interpretability and Explainability and a lot more. There comes the need of new state-of-the Art Deep Learning models like EfficientNet .In this blog we will investigate the superior performance of EfficientNets compared to their counterparts and uncover the reasons “why” dedicating your time to understanding them is unquestionably worthwhile.
In a nutshell EfficientNet-B7 achieves cutting-edge performance on ImageNet with 84.4% top-1 and 97.1% top-5 accuracy. Surprisingly, it accomplishes this despite being 6.1x faster in inference speed and 8.4x smaller than the best available ConvNet(CNNs). Aside from that, EfficientNets have remarkable transfer learning abilities, reaching state-of-the-art accuracy on the CIFAR-100 (91.7%), Flowers (98.8%), and three additional transfer learning datasets, all with a lot less parameters. The primary advantages of EfficientNet(s) is their better precision and efficiency, resulting in them not only more accurate than their competitors but also lighter and faster to perform.
What sets EfficientNet apart and enables it to achieve such remarkable performance?
Compound scaling is what differentiates EfficientNet and enabling it to achieve its amazing results, is the response to the query.
Now, you may ask ,what is Compound Scaling?
Convolutional neural networks (ConvNets) scaling up typically involved adjusting one of three dimensions: depth (number of layers), width (number of channels), or image resolution (image size). This was the case before the invention of EfficientNets. EfficientNets, on the other hand, posed a challenge to this traditional method by rethinking the scaling procedure. They focused on whether there was a systematic approach to scale ConvNets that could improve accuracy and efficiency at the same time.
They learned the value of balancing all three dimensions — network width, depth, and resolution — through empirical research. Surprisingly, by applying a constant scaling ratio to each of these dimensions, this balance could be achieved. Based on this realization, they developed a simple but incredibly powerful technique known as “compound scaling.” Contrary to the conventional method, that adjusts these variables at random, compound scaling uniformly.
The core concept of Compound Scaling truly distinguishes EfficientNets from earlier models. Intuitively, it aligns with the notion that as the input image size increases (resolution), the network should adapt by incorporating more layers (depth) and channels (width) to effectively capture finer details in larger images.Remarkably, the idea of Compound Scaling isn’t limited to EfficientNets alone. It also proves to be applicable and beneficial when applied to existing architectures like MobileNet and ResNet. This demonstrates the versatility and effectiveness of the Compound Scaling approach in enhancing the performance of various neural network architectures.
EfficientNets offer significant advantages in terms of performance and efficiency, they are not without tradeoffs. One key element of these networks is the modified inverted bottleneck block, which extends the capabilities of depthwise convolution. This design choice, like any other, comes with its own set of tradeoffs and considerations in terms of architectural complexity and computational requirements.
Conclusion :
In conclusion, EfficientNet stands as a groundbreaking achievement in the realm of convolutional neural networks. Its innovative approach to compound scaling, effectively balancing network width, depth, and resolution, has redefined the standards for achieving top-tier accuracy and efficiency in computer vision tasks. By combining the strengths of these dimensions and applying them uniformly, EfficientNets offer superior performance while remaining lightweight and quick to execute.
EfficientNets have demonstrated their prowess by achieving state-of-the-art results on ImageNet and a range of transfer learning datasets. Their versatility extends beyond novel architectures, as the compound scaling concept has also proven effective when applied to existing models like MobileNet and ResNet.
While not immune to tradeoffs, such as the architectural complexities introduced by modified inverted bottleneck blocks, EfficientNets undoubtedly represent a significant advancement in the field of deep learning. As the demands for accuracy and efficiency continue to grow in various real-world applications, EfficientNet’s legacy as a cornerstone in the evolution of convolutional neural networks remains firmly established.
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