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Overview

Training is done 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/

Repository:

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

Software

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: Python Repository

Navigate into the training directory and run training:

cd training
python -m training

Training will take a few hours.

If training is interrupted, change the model load path:

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

  • data=MODEL_RESUME_PATH

Setup

Follow the instructions: Python Repository

Install packages: pip install -r training/requirements.txt

  • Do not use the other requirements files

Navigate into the training directory and run training:

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.

Open the Ultralytics configuration file:

  • Windows: C:/Users/[Username]/AppData/Roaming/Ultralytics/settings.yaml

  • MacOS: ~/Library/Application Support/Ultralytics/settings.yaml

  • Linux: ~/.config/Ultralytics/settings.yaml

Go to the directories of the 1st 3 lines and delete the directory there:

  • Example: runs_dir: C:\Users\WARG\model-training\runs , so go to model-training and delete runs

Change the 1st 3 lines to this:

datasets_dir: C:\Users\WARG\Ultralytics\datasets
weights_dir: C:\Users\WARG\Ultralytics\weights
runs_dir: C:\Users\WARG\Ultralytics\runs

Or more appropriate directories if desired.

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

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

WARG desktop details: WARG Desktop

CUDA compatibility information: CUDA and Pytorch

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