KittiSeg

Why we included this project

Teams working on camera-based road detection for autonomous driving will find this a compact, well-documented reference for training a fully convolutional network that separates drivable road pixels from everything else. Its most useful trait is data efficiency: the model reportedly exceeds 96% MaxF1 on the KITTI Road benchmark while training on only 250 densely labelled images, a practical lesson for teams that cannot collect huge segmentation datasets. The repository keeps training, evaluation, visualization, and a demo in one place, and the demo runs inference at roughly 95 ms per image, which puts it in range for latency-sensitive pipelines. Since it is built on the TensorVision backend and organized as a clean reusable module, it is also easy to study as a template for your own TensorFlow segmentation experiments before moving to newer frameworks.