Abstract: Using semantic segmentation networks to intelligently categorize remote sensing images (RSIs) is essential for urban planning, land use, and environmental monitoring. However, the complexity ...

Introduction: Weeds compete with crops for water, nutrients, and light, negatively impacting maize yield and quality. To enhance weed identification accuracy and meet the requirements of precision ...

Abstract: Remote sensing semantic segmentation has broad applications in critical fields such as urban planning and environmental monitoring. Semi-supervised semantic segmentation methods tackle the ...

It is a mix of MIT and the official DINOv3 License. All the codebase in this repository are completely open and can be used for research, education, and commercial purposes freely. The models trained ...

While I found the config files and code for training and distilling DinoV3, as well as training the classification head and doing the text alignment, I didn't find training code for semantic ...