Команда Ikanam

Кейс "Определение и классификация дефектов сварных швов с помощью ИИ"

1. Загрузите репозиторий на свой компьютер и откройте её в вашей предпочитаемой среде разработки (IDE).

git clone https://github.com/ikanam-ai/Detection-of-welding-seams.git

2. Откройте терминал в IDE и введите туда следующую команду:

python -m venv .venv
.\.venv\Scripts\activate

3. Дождитесь создание папки .venv затем введите следующую команду:

cd Detection-of-welding-seams/frontend/streamlit

3. Инициализация проекта:

poetry init
poetry update
poetry install

4. Запустите приложение через Poetry:

poetry run streamlit run zapusk.py

Screencast наших сервисов

Пример работы web-сервиса

Часть 1:

Часть 2:

Часть 3:

Пример работы TG-бота

Project Organization

├── LICENSE            <- Open-source license if one is chosen
├── Makefile           <- Makefile with convenience commands like `make data` or `make train`
├── README.md          <- The top-level README for developers using this project.
│
├── docs               <- A default mkdocs project; see mkdocs.org for details
│
├── models             <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks          <- Jupyter notebooks. Naming convention is a number (for ordering),
│                         the creator's initials, and a short `-` delimited description, e.g.
│                         `1.0-jqp-initial-data-exploration`.
│
├── pyproject.toml     <- Project configuration file with package metadata for Detection-of-welding-seams
│                         and configuration for tools like black
│
├── references         <- Data dictionaries, manuals, and all other explanatory materials.
│
│
├── requirements.txt   <- The requirements file for reproducing the analysis environment, e.g.
│                         generated with `pip freeze > requirements.txt`
│
└── Detection-of-welding-seams                <- Source code for use in this project.
    │
    ├── __init__.py    <- Makes Detection-of-welding-seams a Python module
    │
    ├── data           <- Scripts to download or generate data
    │   └── make_dataset.py
    │
    ├── features       <- Scripts to turn raw data into features for modeling
    │   └── build_features.py
    │
    ├── models         <- Scripts to train models and then use trained models to make
    │   │                 predictions
    │   ├── predict_model.py
    │   └── train_model.py
    │
    └── visualization  <- Scripts to create exploratory and results oriented visualizations
        └── visualize.py

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