A state-of-art deep learning model for semantic image segmentation, where the goal is to assign semantic labels (e.g., cat, dog, car) to every pixel in the input image. Developed by Google, it's designed to improve the accuracy of image segmentation, especially at object edges.
A framework built on top of TensorFlow that makes it easy to construct, train, and deploy object detection models. It includes a collection of pre-trained models that have been trained on various image recognition tasks, including the COCO dataset, Kitti dataset, and the Open Images dataset.
View Tool