This is the Windows app named RefineNet whose latest release can be downloaded as refinenetsourcecode.zip. It can be run online in the free hosting provider OnWorks for workstations.
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RefineNet
DESCRIPTION
RefineNet is a MATLAB-based framework for semantic image segmentation and general dense prediction tasks. It implements the architecture presented in the CVPR 2017 paper RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation and its extended version published in TPAMI 2019. The framework uses multi-path refinement and improved residual pooling to achieve high-quality segmentation results across multiple benchmark datasets. It provides trained models for datasets such as PASCAL VOC 2012, Cityscapes, NYUDv2, Person_Parts, PASCAL_Context, SUNRGBD, and ADE20k, with versions based on ResNet-101 and ResNet-152 backbones. The repository supports both single-scale and multi-scale prediction, with scripts for training, testing, and evaluating segmentation performance. While this codebase is specific to MATLAB and MatConvNet, a PyTorch implementation and lighter-weight variants are also available from the community.
Features
- Implements RefineNet for high-resolution semantic segmentation
- Provides trained models on seven benchmark datasets
- Supports single-scale and multi-scale prediction with fusion
- Uses improved residual pooling for better segmentation accuracy
- Includes training and evaluation scripts for custom datasets
- Compatible with ResNet-101 and ResNet-152 backbones in MatConvNet
Programming Language
C++, MATLAB, Python, Unix Shell
Categories
This is an application that can also be fetched from https://sourceforge.net/projects/refinenet.mirror/. It has been hosted in OnWorks in order to be run online in an easiest way from one of our free Operative Systems.
