dromara/Omega-AI

What it solves

Omega-AI is a deep learning framework written entirely in Java. It aims to make AI development more accessible to Java developers and provides a way to deeply understand the internal mechanics of algorithms and models without relying on external third-party APIs for its core engine.

How it works

The framework allows users to build neural networks by stacking various layer types (fully connected, convolutional, RNN, LSTM, Transformer, etc.) and configuring optimizers (Adam, SGD, RMSProp) and loss functions. It supports GPU acceleration via CUDA and cuDNN through the JCuda library, enabling multi-GPU training for large-scale models.

Who it’s for

Java developers who want to implement, train, and test deep learning models within the Java ecosystem, as well as those looking to study the architecture of frontier AI models.

Highlights

• Broad Model Support: Implements architectures including VGG16, ResNet, YOLO, LSTM, Transformer, GPT, Llama, and Diffusion/Stable Diffusion.
• Hardware Acceleration: Supports CUDA and cuDNN for GPU-accelerated training and inference.
• Comprehensive Tooling: Includes a wide array of activation functions (ReLU, GeLU, SiLU), normalization layers (Batch, Layer), and learning rate schedulers.
• Built-in Demos: Provides practical examples for MNIST digit recognition, YOLO object tracking, and text-to-image generation.