Krishnateja244/YoloV9_instance_segmentation_using_SAM
Comparision of YOLOV9 instance segmentation and SAM based segmentation on remote sensing images
This project helps remote sensing analysts or environmental scientists automatically identify and outline objects in satellite or aerial imagery. You input remote sensing images, and it outputs images where specific objects (like buildings, trees, or vehicles) are precisely highlighted and separated from the background. This is useful for anyone who needs to extract detailed information from geospatial visuals.
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Use this if you need to accurately segment and differentiate individual objects within high-resolution remote sensing images for tasks like land use mapping or environmental monitoring.
Not ideal if you are looking for a pre-trained, production-ready solution with extremely high accuracy, as the models provided are noted to be trained for fewer epochs due to computational constraints.
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May 06, 2024
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