Overview

Training is run on the WARG desktop on the Windows partition. A default Ultralytics model is loaded (e.g. nano) with no initial weights. Training configurations are described here: https://docs.ultralytics.com/usage/cfg/

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https://github.com/UWARG/model-training

Software

Setup

Follow the instructions: Autonomy Workflow Software

Install packages:

pip install -r

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 requirements.txt

• Do not use the other requirements files

Navigate into the training directory and run trainingRun initial training to create the configuration files:

cd training
python -m training

If it reaches the dataset checking phase, press ctrl-c to stop the program. It will probably fail before that point with a file not found error.

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Use other directories if desired.

Usage

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

C:\Users\WARG\Ultralytics\datasets\[test, train, val]

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 the environment: Autonomy Workflow Software

Navigate into the training directory repository and run training:

cd training
python -m training

Training will take a few hours.

If training is interrupted, change the model load path in training.py :

MODEL_RESUME_PATH = C:\Users\WARG\Ultralytics\runs\[latest training number]\last.pt

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    model.train(
        data=MODEL_RESUME_PATH,
        ...,
    )

Where [latest training number] is the number of the checkpoint.

Hardware

Each epoch takes approximately 5 minutes to complete on an NVIDIA GeForce RTX 2060 with 6GB VRAM: https://www.techpowerup.com/gpu-specs/geforce-rtx-2060.c3310

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