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Train custom machine learning models with a free, private desktop application.

Lobe is a simplified, yet powerful, desktop application developed by Microsoft designed to democratize machine learning by removing the barriers of coding and data science expertise. Primarily focused on image classification, Lobe utilizes a local-first architecture where model training occurs entirely on the user's hardware, ensuring data privacy and eliminating cloud-related costs. The technical foundation leverages transfer learning and automated model selection, where the software identifies the optimal neural network architecture (typically variants of ResNet or MobileNet) based on the provided dataset. In the 2026 market, Lobe remains a vital bridge for IoT developers and edge computing engineers, providing seamless export capabilities to formats such as TensorFlow Lite, CoreML, and ONNX. This allows for immediate deployment on mobile devices, Raspberry Pi, and other low-power hardware. While it specializes in classification, its ability to handle iterative refinement through a real-time 'Play' mode makes it an industry standard for rapid prototyping in manufacturing quality control, ecological monitoring, and interactive UI/UX research. It represents the pinnacle of accessible AI, focusing on the human-centric aspects of labeling and refining rather than the underlying mathematical complexity.
Lobe is a simplified, yet powerful, desktop application developed by Microsoft designed to democratize machine learning by removing the barriers of coding and data science expertise.
Explore all tools that specialize in train machine learning models. This domain focus ensures Lobe delivers optimized results for this specific requirement.
Explore all tools that specialize in image classification. This domain focus ensures Lobe delivers optimized results for this specific requirement.
Executes the entire training pipeline on the user's machine, meaning data never leaves the device.
Allows users to provide immediate feedback on model predictions, which are then fed back into the training loop.
Uses heuristics to select the best pre-trained model architecture based on the dataset size and variety.
Runs a local API server that allows external applications to send images and receive predictions.
Compiles models into specialized formats including TensorFlow, CoreML, and ONNX.
Provides visual feedback on label distribution to prevent model bias.
Direct interface with system cameras for live dataset collection.
Download and install the Lobe desktop application for Windows or macOS.
Create a new project and define your classification labels.
Import training images by dragging and dropping folders or using a connected webcam.
Label your images accurately; Lobe provides suggestions based on visual similarity.
Automatic training begins instantly in the background using local CPU/GPU resources.
Use the 'Play' tab to test the model with new images and verify accuracy in real-time.
Refine the model by correcting any misclassifications directly within the 'Play' interface.
Review the 'Optimize' settings to choose between faster inference or higher accuracy.
Navigate to the Export tab and select your target platform (e.g., iOS, Android, or Python).
Integrate the exported model files into your application or use Lobe Connect for local web requests.
All Set
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Verified feedback from other users.
"Users praise Lobe for its incredible ease of use and privacy-centric approach, though some note it is limited currently to image classification tasks."
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