The Gym is a toolbox to segment imagery with a variety of a family of UNet models, facilitating fully reproducible label-to-model workflows.
Semantic-segmentation
- This blog post introduces image segmentation and clarifies the different types of segmentation (semantic, instance and panoptic) and discusses annotation for segmentation projects.
- This repository contains the code and configuration files to reproduce semantic segmentation results of Swin Transformer.
- This article aims to demonstrate how to semantically segment aerial imagery using a U-Net model defined in TensorFlow.
- This repository contains a description of the dataset and how to use it. It also contains example code to get a working segmentation model up and running quickly using a small sample dataset.
- This project maps tree extent at the ten-meter scale using open source artificial intelligence and satellite imagery. Learn more here.
- Sen4AgriNet is a harmonized multi-country, multi-temporal benchmark dataset for agricultural earth observation machine learning applications. Learn more here.
- Microsoft's Bing Maps has released datasets of building footprints around the world. The data is freely available for download. Check here for details.
- Check out this new method and impressive results of unsupervised semantic segmentation.
- Million-AID is a large-scale benchmark dataset containing a million instances for remote sensing scene classification.
