Skip to content

Robopipe/Studio

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

87 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

🚀 Advanced Industrial Machine Vision & AI Quality Inspection

Revolutionizing automated defect detection and smart manufacturing through Deep Learning and Edge AI.

Robopipe Studio is an open-source software designed for capturing and processing image data, labeling images, and training and deploying offline machine learning models on Edge-Compute hardware (Luxonis). It provides a user-friendly interface for managing image datasets, annotating images, and building offline computer vision applications.

📹 Capture, Label, Train and Infer

Operators can label and fine-tune datasets using an intuitive interface, specifically engineered to handle complex, non-rigid products where traditional rule-based vision systems fail. Optimized models are deployed via robopipe API to Edge-Compute hardware (Luxonis) for real-time inference in manufacturing processes.

Capture Capture Label Label
Train Train Inference Infer

📑 Documentation

To learn more about Robopipe Studio, please visit the Robopipe Documentation.

🛠 Running the app

Using Docker

Prerequisites

  • Docker
  1. Clone the repository:

    git clone https://github.com/Robopipe/Studio.git
  2. Build the Docker image:

    cd Studio
    docker build -t robopipe-studio .
  3. Run the Docker container:

    docker run -p 8000:8000 robopipe-studio

From source

Prerequisites

  • Python 3.8 or higher
  • Git
  1. Clone the repository:

    git clone https://github.com/Robopipe/Studio.git
  2. Navigate to the project directory:

    cd Studio
  3. Install the required dependencies:

    a) Install dependencies for API:

     python3 -m venv .venv
     source .venv/bin/activate
     python3 -m pip install poetry
     poetry install
     python3 label_studio/manage.py collectstatic

    (optional) Install base NN models:

     python3 label_studio/manage.py installmodels --all

    b) Install dependencies for Frontend:

     cd web
     yarn install
  4. Build the frontend:

    yarn ls:build
  5. Run the application:

    cd ..
    python3 label_studio/manage.py runserver

📬 Feedback

Robopipe values all your feedback. If you encounter any problems with the app, please open a GitHub issue for anything related to this app - bugs, improvement suggestions, documentation, developer experience, etc.

👫 Community

Join our Robopipe subreddit to share your apps, ask any questions regarding Robopipe, get help debugging your apps, or simply to read more about Robopipe from our users.

About

Automated defect detection and smart manufacturing through Deep Learning and Edge-Compute cameras.

Topics

Resources

License

Code of conduct

Contributing

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Contributors 2

  •  
  •