ResNet50 is a deep convolutional neural network (CNN). It is a modification of the ResNet developed by Microsoft Research in the year, 2015. A version of the popular ResNet architecture is abbreviated as ‘ResNet,’ is what shallowing of Transfer Learning entails. The 50 layer in the network is where the name “ResNet” derives from, in this exemplar.
ResNet50 is a powerful image classification model that trains on large databases of images and reaches its performance at a very high level. The feature that the model puts forward to break the hurdle of the connectivities lies in the use of the residual connections. It allows the network to learn a set of residual mapping functions that map the input to the desired output. These residual connections allow the network to understand much deeper architects. It will achieve more complex hierarchies, otherwise, the problem of vanishing gradients might arise.
Architecture of ResNet50:
There are four ain’t parts of the architecture of ResNet50: convolutional layers, identity block, convolutional block, and network layers. The convolutional layer collects features from the incoming images and the identity block and convolutional block process and converts them. In the last stage, the feature-packed layers are employes to categorize the query fully.
Convolutional layers:
The convolutional layers in ResNet50 perform the actual work on extracted features. The three convolutional layers follow batch normalization and ReLU activation. These layers are in charge of visual input extraction from the initial image, such as lines, contours, tonal patterns, and shapes. The convolutional outputs flow into the max pooling layers that lower the spatial dimensions of feature maps while making sure the important elements remain in the network.
Identity block and convolutional block:
Identity block and convolutional block are the two main constituents of ResNet50 of which the latter comes in compact versions (1 × 1 convolution) and full-sized versions (3 × 3 convolutions). An identity block is a common block that includes the input of the net and the result of several convolutional layers. Finally, the input is added back to the output. The network can therefore learn functions of a residual nature that map the input to the units to which it is connected. The convolutional block is similar to the identity block. It has an additional operation to the layer where the 1×1 convolutional layer reduces the number of filters before the application of the 3×3 convolutional layer.
Fully connected layers:
In the last section of ResNet50, Fully connected layers must be featured. These findings are the sky of the ultimate classification. In the last fully connected layer, the result passes to the softmax activation function where the class probabilities are produced in the final output. ResNet50 is developed using large data sets on different benchmarks which have led to its best performance. It trains by the ImageNet dataset rich with 14 million particularly labeled images and 1000 classes. The error rate on such a dataset has decreased from 22 for ResNet50. 85% paralleling human performance having similarity that is 5, which is a mistake rate being equal to 5. 1%.
ResNet50 Model Architecture:
Residual connections such as as “skip connections” allocate a major position in the architecture of ResNet50. They are there to help the network work in the deeper architectures by eliminating the problem of vanishing gradients.
Yann LeCun, Director of AI Research at Facebook shared their opinion in this way; ResNet50 was an important milestone in neural network technology. It was one of the first networks complete with deep residual blocks. Its success paved the way for training other even bigger networks and pushed the limits of what is possible in computer vision.
How ResNet50 addresses the issue of vanishing gradients:
Disappearing gradients are a place where deep neural networks get lost in their training. The parameters of the deeper layers become too insignificant, allowing the layers to learn and refine themselves. This issue becomes more of a problem due to the increased depth of the network.
The problem can be the full information from the input to the output would pass through one or more layers thus skipping the connection. Thus, the network’s job is to learn residual functions. It maps the input to the desired output in a way that the whole mapping doesn’t have to be found from the beginning.
Identity blocks of ResNet50 contain the skip connections referred to ease more layers and reduce the vanishing gradient problem. An identity block applies a convolutional layer to the input sequence, which converts it through a series of convolutions. Then, adds the convolutional output to the next element in the output sequence. The convolutional block processes the input through a 1×1 convolutional layer to reduce the number of filters and then uses a 3×3 convolutional layer, finally adding the input to the final output.
Through skip connections in ResNet50, the network can move to a deeper architecture while compressing and vanishing the gradients during the process.
Conclusion:
ResNet50 is a high-end deep convolutional neural network that was developed by a crew of Microsoft Research individuals in 2015. A vanishing gradient problem occurs where the derivatives become very small to the point of being close to zero. ResNet-50 is an enhancement of the widely used ResNet. It contains 50 layers that enable ResNet to train deeper architectures effectively than before.
The architecture of ResNet50 is divided into four main parts. The resulting output is fed into an identification block, a convolutional block, a fully connected layer, and a classifier submodel section. The convolutional layer acts as the main feature detector and transforms input to different intermediate layers. The fully connected layer finalizes and classifies the processed feature.
ResNet50 was trained with the ImageNet dataset, which has one of the largest data volumes. It indicates that the model stands on par with the results of human participation. Therefore being very useful in many different image classification tasks such as object detection, facial recognition, medical image analysis, and others. It impounds as a model feature extractor for other operations e.g., object detection, and semantic segmentation.
