Training a model to detect text from the ground up could be a very hard and frustrating task. Conventional way is to use R-CNN with Feature Pyramid Network or using algorithms like YOLO.
Either of them is very hard to implement if you are not aware of the mathematics and logic behind these.
Detectron 2 which is developed by Facebook AI research team is a state-of-the-art object detection model which is based on mask-r-CNN benchmark. It is powered by non-other than Pytorch deep learning framework. Key feature includes
- Panoptic Segmentation: Another product of FAIR, is a type of segmentation That unifies the typically distinct tasks of semantic segmentation (assign a class label to each pixel) and instance segmentation (detect and segment each object instance).
2. Dense pose: Used to map all human pixels of an RGB image to the 3D surface of the human body. This is powered by caffe2.
“This model is meant to advance object detection by offering speedy training and addressing the issues companies face when making the step from research to production”
#deep-learning #ai #machine-learning #data-science #data-visualization
