This repository contains the code of a naive reference solution proposed for the Welding Quality Detection challenge. This solution is provided as a pedagogical example only and should not be considered a benchmark in terms of performance.
To create a virtual environnement, you can use many different tools as python virtual environments (venv), conda or uv. Conda and uv has the advantage of making possible to choose the python version you want to use in your virtual env by setting X.Y in the commands below:
If using python integrated venv , to create a new virtual environnement, type in a terminal :
python -m venv path_to_your_env
If using conda :
conda create -n path_to_your_env python=X.Y
If using uv :
uv venv path_to_your_env --python=X.Y
On Windows power shell
./path_to_your_env/Scripts/activate
On Linux:
source path_to_your_env/bin/activate
To install the package Challenge Welding and its dependencies, from the root directory of this repository type:
pip install .
pip install -r requirements.txt
The scripts and Jupyter notebooks are provided to guide participants through the different utilities developed to facilitate dataset manipulation and AI component development and evaluation:
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01-Tutorial.py: This script demonstrates how to use the main user functions present in this package. It includes examples of how to list available datasets, explore metadata, and draw basic statistics on contextual variables. -
02-Create_pytorch_dataloader.py: This script shows how to use the package to create a PyTorch dataloader. -
03-Test_AIComponent.py: This script demonstrates how to load an AI component and evaluate it by generating operational and uncertainty metrics.
These scripts are also available as jupyter notebooks, where we provide more information concerning the use case, the dataset, and the AI component to be developed and evaluated:
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01-Tutorial.ipynb -
02-Create_pytorch_dataloader.ipynb -
03-Test_AIComponent.ipynb
