Lobe

Lobe is a free, beginner-friendly desktop tool that lets you train machine learning models on Mac and PC and ship them to various platforms without writing code. It emphasizes a visual workflow for building, training, and exporting ML models, making it accessible for developers and non-developers alike.

---

Overview

• Free, easy-to-use machine learning platform designed to simplify model creation and deployment.
• Desktop application focused on a no-code/low-code workflow to train models and export them to supported runtimes.
• The project showcases a collection of repositories and tooling to work with Lobe models across languages and environments.

---

How It Works

1. Create or import a dataset suitable for your task (image-based datasets are common with Lobe).
2. Train a model visually using the built-in training pipeline that requires minimal or no coding.
3. Export or deploy the trained model to various targets and platforms via included starters and libraries.

Note: The Lobe desktop application is no longer under active development, but the open-source ecosystem and related tooling remain available for users who wish to explore or adapt the project.

---

Repositories & Tooling in the Lobe Ecosystem

• lobe: Core desktop application for training and exporting models.
• lobe-python: Python toolset for working with Lobe models programmatically.
• lobe.NET: .NET library for integrating Lobe models into .NET applications.
• image-tools: Tools for creating image-based datasets for machine learning.
• ios-bootstrap: Starter project to bootstrap an iOS app that uses Lobe models.
• android-bootstrap: Starter project to bootstrap an Android app that uses Lobe models.
• web-bootstrap: Starter project to bootstrap a web application that uses Lobe models.
• flask-server: REST API starter project for serving Lobe models via Flask.
• lobe-adafruit-kit: Kit to bring ML ideas to life with Adafruit hardware.
• docusaurus: Documentation framework used for maintaining open-source docs.

The repositories span multiple languages (Swift, Python, C#, TypeScript, Kotlin, etc.) and provide a pathway to integrate Lobe-trained models into mobile, web, and embedded environments.

---

How to Use (High-Level)

• Install the Lobe desktop app or use the available starter projects.
• Import or assemble a dataset, configure a training workflow, and train a model through the visual interface.
• Export the trained model to formats compatible with your target platform using the provided exporters or starter templates.
• Integrate the exported model into your application using the corresponding SDKs or libraries.

---

Safety & Considerations

• While Lobe aims to simplify ML model creation, users should ensure data privacy and proper licensing for datasets.
• Since the desktop app is not actively developed, some modern OS compatibility or features may require workarounds or community guidance.

---

Core Features

• Free, beginner-friendly desktop app for training ML models without heavy coding
• Visual, drag-and-drop workflow to build, train, and export models
• Cross-platform export options to mobile, web, and embedded environments
• Rich ecosystem with Python, .NET, web, and mobile tooling
• Image-based dataset tooling to streamline dataset creation
• Starter projects for iOS, Android, web, and REST API deployment
• Open-source components and community-driven documentation