Implemented UNet and PSPNet architectures using Tensorflow on idd20k_lite dataset. The idd20k_lite dataset has 7 classes that include Drivable, Non-Drivable, Living things, Vehicles, Road-side objects, Far-objects, and Sky. The segmentation challenge was the pixel-level prediction of all the 7 classes at level 1 of the label hierarchy.
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Implemented UNet and PSPNet architectures using Tensorflow on idd20k_lite dataset. The idd20k_lite dataset has 7 classes that include Drivable, Non-Drivable, Living things, Vehicles, Road-side objects, Far-objects, and Sky. The segmentation challenge was the pixel-level prediction of all the 7 classes at level 1 of the label hierarchy.
yashmarathe21/semantic-segmentation-on-indian-driving-dataset
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Implemented UNet and PSPNet architectures using Tensorflow on idd20k_lite dataset. The idd20k_lite dataset has 7 classes that include Drivable, Non-Drivable, Living things, Vehicles, Road-side objects, Far-objects, and Sky. The segmentation challenge was the pixel-level prediction of all the 7 classes at level 1 of the label hierarchy.
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