deeplabv3 and pytorch-deeplab-resnet
About deeplabv3
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.
About pytorch-deeplab-resnet
isht7/pytorch-deeplab-resnet
DeepLab resnet v2 model in pytorch
This project helps researchers and engineers who work with visual data by providing a way to segment images, identifying distinct objects or regions within them. You feed in images and their corresponding ground truth labels, and it outputs a trained model that can then predict pixel-level classifications for new images. It is used by computer vision practitioners focused on tasks like semantic segmentation.
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