This tutorial shows how to use Keras library to build deep neural network for ultrasound image nerve segmentation. More info on this Kaggle competition can be found on https://www.kaggle.com/c/ultrasound-nerve-segmentation.
Principalele funcționalități ale jocicmarko/ultrasound-nerve-segmentation sunt: Computer Vision Models, Deep Learning Models, Segmentation Architectures.
Alternativele open-source pentru jocicmarko/ultrasound-nerve-segmentation includ: divamgupta/image-segmentation-keras — Implementation of various Deep Image Segmentation models in keras. elliottd/groundedtranslation — #GroundedTranslation. hyeonwoonoh/deconvnet — DeconvNet : Learning Deconvolution Network for Semantic Segmentation. facebookresearch/maskrcnn-benchmark — This project is a modular PyTorch framework for training and evaluating object detection and instance segmentation… isht7/pytorch-deeplab-resnet. tdeboissiere/deeplearningimplementations — Implementation of recent Deep Learning papers.
Implementation of various Deep Image Segmentation models in keras.
This project is a modular PyTorch framework for training and evaluating object detection and instance segmentation models. It serves as a computer vision research tool and a deep learning inference engine designed to identify object locations, classes, and pixel-level masks within images. The framework implements a two-stage inference pipeline that utilizes region proposal networks and a symmetric mask-head architecture. It provides specialized capabilities for instance segmentation, object bounding box detection, and human pose estimation via anatomical keypoint detection. The system includ
DeconvNet : Learning Deconvolution Network for Semantic Segmentation
