Mask R CNN
The structure of Mask R-CNN consists of three main components:
Backbone network: This component provides a feature extractor that is responsible for generating feature maps from the input image. Commonly used backbone networks include ResNet, VGG, and Inception.
Region Proposal Network (RPN): This component takes the feature maps generated by the backbone network and generates regions of interest (ROIs) that contain objects of interest.
RoIAlign and classifier head: The ROIs generated by the RPN are fed into the RoIAlign layer, which performs region-of-interest (ROI) pooling to generate fixed-length feature vectors. These feature vectors are then passed through a classifier head, which predicts the class labels, object bounding boxes, and object masks for each ROI.
Overall, Mask R-CNN works by using the backbone network to extract features from the input image, using the RPN to generate ROIs, and using the RoIAlign layer and classifier head to predict object instance segmentation information.
*Description created by ChatGPT
