Pinned Repositories
4474-Group-26
4474-Group 26
deepglobe_land_cover_classification_with_deeplabv3plus
DeepGlobe Land Cover Classification Challenge遥感影像语义分割
dubai-satellite-imagery-segmentation
Multi-Class Semantic Segmentation on Dubai's Satellite Images.
EasyCooking
LoveDA
[NeurIPS 2021] LoveDA: A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentation
newbook
PaddleSeg
Easy-to-use image segmentation library with awesome pre-trained model zoo, supporting wide-range of practical tasks in Semantic Segmentation, Interactive Segmentation, Panoptic Segmentation, Image Matting, 3D Segmentation, etc.
recipetool
satellite-sam-dashboard
This app integrates the Segment Anything Model (SAM) with Sentinel-2 data. The app is built using Dash Plotly and dash leaflet. It allows segmenting satellite images using the two ways provided by SAM: automatic mask generator and prompt segmentation (using points and bounding boxes).
segment-anything
The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.
Li-sde's Repositories
Li-sde/4474-Group-26
4474-Group 26
Li-sde/deepglobe_land_cover_classification_with_deeplabv3plus
DeepGlobe Land Cover Classification Challenge遥感影像语义分割
Li-sde/dubai-satellite-imagery-segmentation
Multi-Class Semantic Segmentation on Dubai's Satellite Images.
Li-sde/EasyCooking
Li-sde/LoveDA
[NeurIPS 2021] LoveDA: A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentation
Li-sde/newbook
Li-sde/PaddleSeg
Easy-to-use image segmentation library with awesome pre-trained model zoo, supporting wide-range of practical tasks in Semantic Segmentation, Interactive Segmentation, Panoptic Segmentation, Image Matting, 3D Segmentation, etc.
Li-sde/recipetool
Li-sde/satellite-sam-dashboard
This app integrates the Segment Anything Model (SAM) with Sentinel-2 data. The app is built using Dash Plotly and dash leaflet. It allows segmenting satellite images using the two ways provided by SAM: automatic mask generator and prompt segmentation (using points and bounding boxes).
Li-sde/segment-anything
The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.
Li-sde/segment-geospatial
A Python package for segmenting geospatial data with the Segment Anything Model (SAM)