Feature Fusion Module v2, or FFMv2, is an important module in the object detection model known as M2Det. Its primary function is to combine the features from different levels to create a final, multi-level feature pyramid.
What is M2Det?
M2Det is an object detection model that aims to accurately and efficiently detect objects within an image. The model is based on the concept of feature pyramids, which involves combining features at multiple scales to achieve better accuracy.
What is a feature pyramid?
A feature pyramid is a set of hierarchical features extracted from an image at different scales. The idea is to detect objects at various scales by combining features from different levels of the pyramid.
What is the purpose of FFMv2?
The purpose of FFMv2 is to fuse together features from different levels of the feature pyramid to create a final, multi-level feature pyramid. It does this by using 1x1 convolution layers to compress the channels of the input features and then concatenating these feature maps together.
How does FFMv2 work?
FFMv2 takes the base feature and the largest output feature map of the previous Thinned U-Shape Module (TUM), which are of the same scale, as input. It then combines these features using 1x1 convolution layers to compress the channels and a concatenation operation to aggregate the feature maps. The resulting fused feature is then used as input for the next TUM.
What is a Thinned U-Shape Module?
A Thinned U-Shape Module, or TUM, is a module in the M2Det object detection model that is responsible for generating multi-scale features. The module is designed to create feature maps at different scales by fusing together features from different levels of the feature pyramid.
Why is FFMv2 important?
FFMv2 is important because it allows the M2Det model to achieve better accuracy in object detection by combining features from multiple levels of the feature pyramid. This results in a more robust and accurate model that can detect objects at different scales.
Overall, Feature Fusion Module v2 is an important module in the M2Det object detection model that helps create a final, multi-level feature pyramid by fusing together features from different levels. This leads to better accuracy in object detection and a more robust and accurate model.
