Computer Vision: How to Do Object Detection and Segmentation with the latest Mask R-CNN Algorithm
Mask R-CNN is a Deep Learning method for computer vision systems. It extends Faster R-CNN and adds a branch for predicting an object mask in parallel with the existing branch for bounding box recognition. Mask R-CNN is simple to train and adds only a small overhead to Faster R-CNN, running at 5 fps. Moreover, Mask R-CNN is easy to generalize to other tasks. For exmaple it allowing us to estimate human poses in the same framework. Mask R-CNN outperforms all existing, single-model entries on every task, including the COCO image dataset. The simple and effective approach will shttp://cocodataset.org/#homeerve as a solid baseline and help ease future development in instance-level recognition.
The COCO dataset contains 80 object categories:Computer Vision: Object Detection and Segmentation with Mask R-CNN #ComputerVision #AI #ArtificialIntelligence #ML #DeepLearning #TransferLearning #ObjectSegmantation #RCNN #Industrie40 #Industry40 Klick um zu Tweeten
The underlying demo video was shot in Frankfurt am Main (Germany). Hope you like it.
Code Example can be downloaded on Github DETECTRON.
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