Papers with Code - ResNet (2024)

FAQs

What is the downside of ResNet? ›

However, one disadvantage of using ResNet is the potential disappearance of gradients in very deep networks, which can make the gradient descent process slow. This can hinder the training of the network, especially as the number of layers increases ⁴.

curve basically stabilizes when the number of iterations is greater than 2000. The ResNet-50 model training accuracy rises to 97% from 65.6%, and the final model training accuracy reaches 99.61% when the number of iterations is between 200 and 450, which is shown in the ResNet-50 model in Figure 8.

The Skip Connections between layers add the outputs from previous layers to the outputs of stacked layers. This results in the ability to train much deeper networks than what was previously possible. The authors of the ResNet architecture test their network with 100 and 1,000 layers on the CIFAR-10 dataset.

What problems ResNets solve? One of the problems ResNets solve is the famous known vanishing gradient. This is because when the network is too deep, the gradients from where the loss function is calculated easily shrink to zero after several applications of the chain rule.

For classification, you can use any pre-trained network such as ResNet, VGG, InceptionV3, and so on. This helps in reducing computational costs. For image segmentation, U-Net can be used. This helps in retaining the spatial features of the image in better segmentation results.

ResNet is superior to CNN because it introduces the concept of residual units, which allows deep layers to directly learn from shallow layers, reducing the difficulty of network convergence. This results in better learning ability and improved performance in image recognition tasks ¹.

While Inception focuses on computational cost, ResNet focuses on computational accuracy. Intuitively, deeper networks should not perform worse than the shallower networks, but in practice, the deeper networks performed worse than the shallower networks, caused not by overfitting, but by an optimization problem.

VGGNet uses a deep stack of 3x3 convolutional layers for image recognition. The focus of the network is the depth of the straightforward architecture. Meanwhile, ResNet incorporates residual connections, which helps train much deeper networks by addressing vanishing gradient issues.

ResNet architecture, which incorporates residual connections, significantly outperforms prior state-of-the-art models on image recognition tasks such as ImageNet. The authors demonstrate that residual connections help alleviate the vanishing gradient problem and enable much deeper networks to be trained effectively.

Which ResNet model is best? ›

Therefore, each of the 2-layer blocks in Resnet34 was replaced with a 3-layer bottleneck block, forming the Resnet-50 architecture. This has much higher accuracy than the 34-layer ResNet model. The 50-layer ResNet-50 achieves a performance of 3.8 bn FLOPS.

ResNet, short for Residual Network is a specific type of neural network that was introduced in 2015 by Kaiming He, Xiangyu Zhang, Shaoqing Ren and Jian Sun in their paper “Deep Residual Learning for Image Recognition”.

The limitations of the ResNet50 model include data imbalance, overfitting, and lower efficiency in detecting small objects ². Additionally, the ResNet50 model suffers from a lack of precision ³.

Compared with the widely used ResNet-50, the EfficientNet-B4 used similar FLOPS, while improving the top-1 accuracy from 76.3% of ResNet-50 to 82.6% (+6.3%). This indicates that EfficientNet is not only more accurate but also more computationally efficient than existing CNNs.

The depth of ResNet for best accuracy is over four times deeper than previous deep networks. Achieved 3.57% top 5 error rate on the test set with 152 layer ResNet on ensemble model.