Welcome to multim! This is an extensible framework designed for experimentation with deep learning algorithms. You can work with various data types to classify data into two categories, all without needing programming skills.
- User-Friendly Interface: Navigate easily through the application.
- Multiple Data Modalities Support: Experiment with different types of data seamlessly.
- Pre-built Models: Access ready-to-use models for quick results.
- Extensible Architecture: Customize and extend functionality as needed.
To run multim, ensure your computer meets the following:
- Operating System: Windows 10 or newer, macOS Mojave or newer, or a modern Linux distribution.
- RAM: At least 4 GB.
- Storage: Minimum of 500 MB free disk space.
- Python Version: 3.7 or later must be installed on your machine.
- Visit the Releases page to download the latest version of multim.
- Look for the file that matches your operating system.
- Click on the file link to start downloading.
- Once downloaded, locate the file in your downloads folder.
- After the download completes, find the file you downloaded.
- Double-click the file to run the application.
- Follow the on-screen instructions to complete the setup.
After you set up the application:
- Open multim.
- Choose the type of data you want to experiment with.
- Load your dataset using the provided options.
- Select a pre-built model or configure your own parameters.
- Start your experiment and view the results.
If you need assistance while using multim, explore the following resources:
- User Manual: Detailed instructions can be found in the Help section of the application.
- Community Forum: Connect with other users to share experiences and solutions.
- Tutorial Videos: Watch step-by-step guides available on our YouTube channel.
We appreciate contributions to multim! If you're interested in helping, check out the guidelines found in the repository for detailed instructions on how to contribute.
For updates and new releases, keep an eye on our Releases page regularly.
If you have questions, please reach out via issues on the GitHub repository or contact us through the provided links on the repository page.