fregu856/deeplabv3
PyTorch implementation of DeepLabV3, trained on the Cityscapes dataset.
This project helps urban planners, autonomous vehicle researchers, and GIS specialists analyze street-level imagery by automatically identifying and outlining objects like roads, buildings, pedestrians, and vehicles. It takes raw street photos or video frames as input and produces a detailed segmentation map, where each pixel is classified and colored according to the object it represents.
816 stars. No commits in the last 6 months.
Use this if you need to accurately segment urban scenes from image data for tasks like mapping, scene understanding, or training autonomous systems.
Not ideal if you are looking to segment images from different domains, such as medical scans or satellite imagery, as it is specifically trained for urban environments.
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816
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181
Language
Python
License
MIT
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Last pushed
Feb 09, 2022
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