This repo is a personal practice for semantic classification of images using Unet.
The Semantic Drone Dataset focuses on semantic understanding of urban scenes for increasing the safety of autonomous drone flight and landing procedures. The imagery depicts more than 20 houses from nadir (bird's eye) view acquired at an altitude of 5 to 30 meters above ground. A high resolution camera was used to acquire images at a size of 6000x4000px (24Mpx). The training set contains 400 publicly available images and the test set is made up of 200 private images.
more details are in Dataset page. Sample of the dataset images can be found in the Data folder.
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After downloading the data, make sure to set your folders path in Configuration.py
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Clone the repository.
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Install requirements describes in requirements.txt.
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Use the Preprocessing.py script to convert RGB masks to single channel mask image.
- This step is time consuming and it is not requierd if you already own a converted version of masks. Once converted all RGB masks - the step is no longer required.
*Steps 1-3 are executed in Colab notebook
All variables and parameters are set and defined in Configurations.py. Tuning models parameters and files paths can be change directly in the Configuration script for personal configurations.
