Open Source Deep Learning Projects
Discover 77 open source Deep Learning repositories from GitHub, each with an analysis of what it does, key features, use cases and alternatives. Deep Learning projects here are most often combined with Machine Learning, Python and PyTorch. Last updated October 3, 2026.
77 repositories · updated October 3, 2026
stable-diffusion-webui: Generate Images with Stable Diffusion
A Gradio-based web interface for generating and editing images with Stable Diffusion. It gives artists and other local users a browser-based workflow for prompts, image-to-image tools, model options, and extensions.
jax: Transform and Accelerate Python Numerical Programs
JAX is a Python library for transforming numerical programs with automatic differentiation, compilation, and vectorization. Use it for high-performance scientific computing and machine learning, especially when workloads need to scale across accelerators.
index-tts-lora: Fine-Tune IndexTTS for Custom Voices
A Python project that adds LoRA fine-tuning workflows to IndexTTS for single- and multi-speaker voice synthesis. It is aimed at users who want to adapt speech generation to speaker audio and improve prosody and naturalness.
infinity: Serve Embedding and Reranking Models via API
Infinity is a Python serving engine for text embeddings, reranking, and selected image, audio, and late-interaction models. It provides an OpenAI-aligned REST API with multiple inference backends for teams deploying models from Hugging Face.
LLMBox: Train and Evaluate Large Language Models
LLMBox is a Python library for training and evaluating large language models through a unified pipeline. It suits researchers and developers who want configurable fine-tuning workflows and a broad set of model and benchmark evaluation options.
wifi-3d-fusion: Sense Motion with WiFi Signals
WiFi-3D-Fusion captures WiFi CSI or RSSI data and visualizes motion in 3D, with optional research bridges for pose estimation and RF field reconstruction. It is aimed at researchers and experimenters with compatible hardware, not production or safety-critical use.
ML-From-Scratch: Learn Machine Learning Through NumPy Implementations
ML-From-Scratch provides transparent Python and NumPy implementations of machine learning algorithms, from regression and clustering to neural networks and reinforcement learning. It is suited to learners who want to inspect how models work rather than use an optimized production framework.
Magenta RT: Live Music Generation on Your Local Device
Magenta RealTime (Magenta RT) is an open-source Python library for live music audio generation on local devices. It allows users to create music using both text and audio prompts, serving as a powerful tool for real-time creative audio exploration. This library is the on-device companion to Google's MusicFX DJ Mode and the Lyria RealTime API.
maestro: Fine-Tune Vision-Language Models
Maestro streamlines fine-tuning for multimodal vision-language models with ready-to-use recipes, a CLI, and a Python API. It suits developers adapting supported models to tasks such as JSON extraction and object detection.
GigaSLAM: Build Large-Scale Monocular Outdoor Maps
GigaSLAM is a monocular SLAM system for mapping and tracking in large, unbounded outdoor scenes using RGB video. It combines depth-assisted pose estimation, loop closure, and hierarchical Gaussian splats, and is aimed at research workflows with CUDA-capable GPUs.
Spotlight: Deep Recommender Models with PyTorch
Spotlight is a Python library built on PyTorch for developing deep and shallow recommender models. It offers a comprehensive set of building blocks for various loss functions, representations, and utilities for handling recommendation datasets. This tool is designed for rapid exploration and prototyping of new recommender systems.
PETSA: Adapt Time-Series Forecasters at Test Time
PETSA is a parameter-efficient test-time adaptation method for time-series forecasting. It updates small input and output calibration modules rather than the full model, targeting non-stationary data with lower adaptation costs.