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Lobe AI

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What is Lobe AI?

Lobe AI is a resource that provides tools and resources for machine learning, particularly focused on image classification. It offers a desktop application that allows users to label examples, train models, evaluate training results, and export models for use in various applications. Lobe AI supports a range of export options, including no-code apps on Microsoft’s Power Platform, calling a local API, adapting starter projects, or working with model files directly. The platform also provides a Python SDK and .NET SDK for running exported models. Lobe AI is free and runs on your computer, with all images and models staying private on your device.

 


 

⚡Top 5 Lobe AI Features:

  1. Image-based datasets: Leverage tools for creating image-based datasets for machine learning, allowing users to label and train models on their own images.
  2. Python toolset: Use Python toolset for working with Lobe AI models, enabling users to interact with their models using Python code.
  3. iOS starter project: Start with an iOS starter project to bootstrap your machine learning model, making it easier to integrate machine learning into iOS applications.
  4. Web starter project: Lobe AI offers a web starter project to bootstrap your machine learning model, allowing users to integrate machine learning into web applications.
  5. Android project: Boilerplate Android project to bootstrap your machine learning model, enabling users to integrate machine learning into Android applications.

 


 

⚡Top 5 Lobe AI Use Cases:

  1. Personal trainer: Use this repository to train a model that recognizes different workout positions, creating an automated personal trainer that counts reps.
  2. Emotional reactions: Lobe AIcan be used to analyze emotional reactions in videos, such as detecting happiness, sadness, or anger.
  3. Bird identification: Identify different bird species based on images, helping users learn about the birds in their environment.
  4. Push-up counter: Count the number of push-ups a user performs during a workout, providing feedback and tracking progress.
  5. Shelf monitoring: Visually monitor shelves and alert users when an item is running low, helping users keep track of their inventory.
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