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Setup

Follow the instructions to clone the repository and activate the environment: Autonomy Workflow Software

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Use other directories if desired.

Note

On Windows, if there is an error along the lines of "path/to/fbgem.dll" or one of its dependencies is missing when importing pytorch, you can check if it’s missing. Usually it is not missing, but its dependency libomp that’s missing. You can install this as part of the Microsoft Visual C++ redistributable. If that still does not work, you can directly download it from here and put it in C:\Windows\System32.

Usage

Move or copy the 3 directories of the dataset so that it is in the dataset directory:

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Make sure that any old datasets are out of this directory or have their test, train, val directories renamed (e.g. test_landing_pad , train-old , val0 )! Hiding them in a directory underneath dataset is not sufficient (e.g. ...\datasets\landing_pad\test might still be erroneously used).

Activate Navigate into the repository and activate the environment: Autonomy Workflow Software Navigate into the repository and run

Run training:

Code Block
python -m training

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  • There is only enough VRAM for nano and small models, not larger ones.

  • PyTorch version 2.4.1 is being used with CUDA 12.4, which has minimum driver requirements. Please ensure that a suitable driver is installed (there should be a high enough driver version for the RTX 2060, you can find them here if needed. (We are not going to use the old Jetson TX2i, but the RPi5 on the drone)

WARG desktop details: WARG Desktop

More detailed CUDA compatibility information: CUDA and PyTorch

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