DenseNet and EfficientNet - How CNNs were made better and better | Computer Vision Series скачать в хорошем качестве

DenseNet and EfficientNet - How CNNs were made better and better | Computer Vision Series

DenseNet vs EfficientNet - Which One Should You Use? In this lecture from the Computer Vision from Scratch series, we dive deep into two powerful CNN architectures – DenseNet and EfficientNet. We begin with a full recap of everything covered so far: from linear models and AlexNet to ResNet-50 and MobileNet. Then we explore what makes DenseNet and EfficientNet special – both in theory and in practice. DenseNet, introduced in 2017, changed the game with its dense connectivity pattern. Every layer receives inputs from all previous layers. This not only improves gradient flow but also reduces the number of parameters through feature reuse. We examine how DenseNet compares against traditional networks and how it achieves implicit regularization by design. EfficientNet, on the other hand, brought a new perspective to scaling CNNs. Introduced by Google, it uses compound scaling to increase the depth, width, and resolution of CNNs in a principled way. Built using Neural Architecture Search (NAS), EfficientNet B0 onwards delivered strong results with relatively fewer parameters. We implement both models on the Five-Flowers dataset using PyTorch and compare: Accuracy (training and validation) Model size (in MB) Number of parameters Modularity of architecture You will also learn where DenseNet sits in the modular vs non-modular architecture spectrum and how EfficientNet’s innovation lies in model scaling, not in repeating modules. This is one of the most comprehensive practical comparisons of modern CNN architectures available online. 📒 Miro notes: https://miro.com/app/board/uXjVInTNVM... 📓 Colab code: https://colab.research.google.com/dri...