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Implementation of various Deep Image Segmentation models in keras.
The main features of divamgupta/image-segmentation-keras are: Computer Vision Models, Deep Learning Models, Segmentation Architectures.
Projects with overlapping indexed features include: jocicmarko/ultrasound-nerve-segmentation — This tutorial shows how to use Keras library to build deep neural network for ultrasound image nerve segmentation.… facebookresearch/maskrcnn-benchmark — This project is a modular PyTorch framework for training and evaluating object detection and instance segmentation… isht7/pytorch-deeplab-resnet. hyeonwoonoh/deconvnet — DeconvNet : Learning Deconvolution Network for Semantic Segmentation. elliottd/groundedtranslation — #GroundedTranslation. tdeboissiere/deeplearningimplementations — Implementation of recent Deep Learning papers.
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.
DeconvNet : Learning Deconvolution Network for Semantic Segmentation
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
