filippogiruzzi/semantic_segmentation
Semantic Segmentation project for Autonomous Driving based on a TensorFlow implementation of UNet
This project helps autonomous vehicle engineers analyze camera footage by identifying and classifying different objects in images, such as roads, cars, and pedestrians. It takes raw images or video frames as input and outputs a pixel-level classification map, enabling precise environmental understanding for self-driving systems. It is intended for machine learning engineers and researchers working on autonomous driving perception.
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Use this if you need to perform real-time pixel-level classification of objects in images for autonomous driving applications.
Not ideal if you are working with non-automotive imaging data or require a simple object detection (bounding box) solution instead of detailed segmentation.
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Python
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Last pushed
Mar 24, 2023
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