When it comes to object detection in computer vision, the Depthwise Fire Module is a new technique that is gaining attention. This module is a variant of the original Fire Module, which has been used for its effectiveness in deep learning models. The Depthwise Fire Module is particularly significant for its improvement in inference time performance, which is an essential factor in real-time applications such as autonomous driving, robotics, and surveillance.
Fire Module
The Fire Module is a well-known module in deep learning that was introduced in the SqueezeNet architecture by for ImageNet classification. It is designed to reduce the number of parameters in the network while maintaining or improving its accuracy. The module consists of a squeeze layer and an expand layer. The squeeze layer applies a small number of filters to the input tensor, reducing the number of feature maps. This operation reduces the computational cost by reducing the computation for each subsequent layer. The expand layer applies a combination of 1x1 and 3x3 convolutions to the output of the squeeze layer. The number of feature maps is increased again, and the network's capacity is increased. This operation allows the model to learn more complex features and patterns.
Depthwise Separable Convolution
Depthwise Separable Convolution(DSC) is a special type of convolution operation that separates the spatial and channel-wise operations in a convolution operation. Usually, a convolution operation involves a cross-correlation between the input tensor and the kernel. A depthwise convolution's kernel evaluates each channel of the input independently, while a point-wise convolution applies a 1x1 kernel to collapse the channels together. A depthwise separable convolution operation combines these two operations into one, i.e., it applies depthwise convolution operation and pointwise convolution as two separate layers, which reduces the number of operations and parameters in the network.
CornerNet-Lite
CornerNet-Lite is a lightweight architecture used for object detection, mostly used in real-time system applications. The CornerNet-Lite architecture consists of a Feature Pyramid Network(FPN) and a modified RetinaNet head. The FPN generates multi-scale feature maps, which try to capture different levels of details for the detections while the head predicts the locations and the categories of the objects. However, this approach has a high computational cost, especially, the head stage that involves a large number of convolutional layers. Therefore, the researchers proposed the use of the Depthwise Fire Module to reduce the number of parameters and control the computational cost.
Depthwise Fire Module
The Depthwise Fire Module(DFM) extends the original Fire Module by replacing the 3x3 and 1x1 convolutions in the expand layer of the Fire Module with depthwise separable convolutions. This application reduces the number of parameters, making the architecture more lightweight and efficient. The DFM mainly consists of depthwise convolution, which applies the DSC operations to the input tensor, followed by a pointwise convolution, where the output channels are mixed before the next layer. The use of DSC in the Depthwise Fire Module makes the network more efficient in terms of computation time and reduces the memory requirements for training and testing. The DFM has been shown to improve the performance of CornerNet-Lite with accuracy improvements while reducing the computation time leading to faster detection of objects. The faster inference time is an essential factor as the faster detection and classification of objects lead to real-time applications such as self-driving cars, robotics, and facial recognition purposes.
In summary, the Depthwise Fire Module is a relatively new technique in deep learning for object detection that replaces the 3x3 convolution and 1x1 convolution of the Original Fire Module with depthwise separable convolutions. It is used to improve Inference Time performance, which is an essential requirement in real-time object detection problems such as self-driving cars, robotics, and many more critical applications. The DFM helps reduce the number of parameters in the architecture and, as a result, reduces the computational cost, which ultimately optimizes the performance of CNN Models, especially in real-world and computationally constrained applications.