A U-Net trained on 25-band NIR hyperspectral driving images improves semantic segmentation over a spectral-only MLP, and the quantized model runs at 27 FPS on a Zynq MPSoC, 2.55 FPS including preprocessing.
https://pypi.org/project/onnx2keras/ (2021)
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Exploring Fully Convolutional Networks for the Segmentation of Hyperspectral Imaging Applied to Advanced Driver Assistance Systems
A U-Net trained on 25-band NIR hyperspectral driving images improves semantic segmentation over a spectral-only MLP, and the quantized model runs at 27 FPS on a Zynq MPSoC, 2.55 FPS including preprocessing.