The submission for this challenge consists of a single .h5 file containing the parameters of a EfficientNetLite0 neural network. This neural network architecture was selected due to is small size and efficiency, which makes it possible to run in space on board of OPS-SAT.
The easiest way to create a submission is by using Tensorflow and importing the neural network architecture like so:
Users that prefer PyTorch over Tensorflow can make use of the conversion utility found in our starter-kit to generate equivalent .h5-files for submission. However, the evaluation (inference on unseen tiles of test-set) server-side is done using Tensorflow and Tensorflow Lite as OPS-SAT is setup to only run quantized Tensorflow Lite models.
After float16 quantization and inference, the Scoring of your submission is computed and you will enter our Leaderboard.
More details about the quantization of the model and the server-side computation of the final score(s) can be found in a dedicated notebook within our starter-kit.