A comparison between VGG16, VGG19 and ResNet50 architecture frameworks for Image Classification (2024)

FAQs

What is the difference between VGG16 and ResNet50? ›

We have concluded that the ResNet50 is the best architecture based on the comparison. These models have provided accuracies of 0.9667, 0.9707, and 0.9733 for VGG16, VGG19, and ResNet50 at epoch 20. The data provided is a real-life data set, sourced from a regional retailer.

The concept of the VGG19 model (also VGGNet-19) is the same as the VGG16 except that it supports 19 layers. The “16” and “19” stand for the number of weight layers in the model (convolutional layers).

Performance: Generally, ResNets perform better than VGG in terms of accuracy, especially on deeper models. They are also faster to train. Transfer Learning Use: ResNet, with its deeper and more complex architecture, is often used in transfer learning for more sophisticated tasks.

VGG-19's architecture contains 144 million parameters, while VGG-16 has 138 million. There are 13 convolutional layers in the VGG-16, five max-pooling layers (22), and two fully-connected layers.

Depth: ResNet enables the creation of very deep neural networks, which can improve performance on image recognition tasks. Fewer Parameters: ResNet achieves better results with fewer parameters, making it computationally more efficient.

VGG16 is a pre-trained deep learning model that has been used for image classification in various domains. One advantage of using VGG16 is its ability to extract high-level features from images, which allows for accurate classification.

The VGG19 model has 19 layers with weights (see Figure 4)), formed by 16 convolutions and 3 fully-connected (fc) layers and its input is an image of size 224 × 224 and 3 channels with its mean RGB value subtracted. The convolutional layers have a small ker- nel size 3 × 3 with 1 pixel of padding and stride.

In particular, VGG19 can achieve an accuracy of 95% with a loss of 17%. Tables 7 and 8 show that our proposed model has marginally higher accuracy and lower loss, which means that the introduced model can accurately classify the images.

VGG Architecture: The VGG-16 architecture is a deep convolutional neural network (CNN) designed for image classification tasks. It was introduced by the Visual Geometry Group at the University of Oxford.

ResNet-50 is CNN architecture that belongs to the ResNet (Residual Networks) family, a series of models designed to address the challenges associated with training deep neural networks. Developed by researchers at Microsoft Research Asia, ResNet-50 is renowned for its depth and efficiency in image classification tasks.

What is the architecture of VGG and ResNet? ›

The VGG architecture consists of blocks, where each block is composed of 2D Convolution and Max Pooling layers. VGGNet comes in two flavors, VGG16 and VGG19, where 16 and 19 are the number of layers in each of them respectively.

Figure 3a,b shows that each parameter of the VGG16 and VGG19 models was tuned in the same way. The resulting accuracy of VGG19 with the data augmentation technique was 97.89% (Figure 6a), which was less accurate (about 1% less) than VGG16 (Figure 6b) for the tested dataset.

For VGG-19 we have 2*64 + 2*128 + 4*256 + 2*4*512 = 5504 neurons. With all of that said, in practice usually one would measure the size of the network in number of parameters (i.e. weights) and number of layers. VGG-19 has 144 million parameters which is notably large.

There are many popular CNN architectures that we can use as a backbone in neural networks. Some of them include: VGGs – includes VGG-16 and VGG-19 convolutional networks with 16 and 19 layers. They proved effective in many tasks and especially in image classification and object detection.

ResNet has many variants that run on the same concept but have different numbers of pooling layers. Resnet50 is used to denote the variant that can work with 50 neural network layers.

What Is VGG16? VGG16 is a convolutional neural network model that's used for image recognition. It's unique in that it has only 16 layers that have weights, as opposed to relying on a large number of hyper-parameters. It's considered one of the best vision model architectures.

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.

ResNet50 is a popular choice for medical image analysis, specifically for breast cancer detection, due to its ability to extract deep features from images and achieve high accuracy .