Open Source BSD Machine Learning Software
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Machine Learning Software for BSD
Browse free open source Machine Learning software and projects for BSD below. Use the toggles on the left to filter open source Machine Learning software by OS, license, language, programming language, and project status.
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AI-Aimbot
CS2, Valorant, Fortnite, APEX, every game
AI-Aimbot is a computer vision project that demonstrates how artificial intelligence can be used to automatically identify and target opponents in video games. The system uses an object detection model based on the YOLOv5 architecture to detect human-shaped characters in gameplay screenshots or video frames. Once a target is identified, the program automatically adjusts the player’s aim toward the detected target, effectively automating the aiming process in first-person shooter games. The project emphasizes that it is intended for educational purposes to illustrate potential vulnerabilities in game design and anti-cheat systems. Because the system relies solely on visual detection rather than reading game memory, it attempts to bypass certain traditional anti-cheat detection methods. -
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Armadillo
fast C++ library for linear algebra & scientific computing
* Fast C++ library for linear algebra (matrix maths) and scientific computing * Easy to use functions and syntax, deliberately similar to Matlab / Octave * Uses template meta-programming techniques to increase efficiency * Provides user-friendly wrappers for OpenBLAS, Intel MKL, LAPACK, ATLAS, ARPACK, SuperLU and FFTW libraries * Useful for machine learning, pattern recognition, signal processing, bioinformatics, statistics, finance, etc. * Downloads: http://arma.sourceforge.net/download.html * Documentation: http://arma.sourceforge.net/docs.html * Bug reports: http://arma.sourceforge.net/faq.html * Git repo: https://gitlab.com/conradsnicta/armadillo-code -
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NeuralNote
Audio Plugin for Audio to MIDI transcription using deep learning
NeuralNote is an open-source audio software tool designed to convert recorded audio into MIDI data using modern machine learning techniques. The software functions as an audio plugin that can be used inside digital audio workstations as well as a standalone application for music production and analysis. Its main purpose is to perform audio-to-MIDI transcription, allowing musicians to record a performance and automatically transform it into editable MIDI notes. NeuralNote supports polyphonic transcription, meaning it can detect multiple notes played simultaneously, making it useful for instruments such as piano or guitar. The system relies on neural network models to analyze audio signals and infer pitch, timing, and other musical attributes that can be represented as MIDI data. The resulting MIDI output can be edited, quantized, or exported to other instruments within a music production workflow. -
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Dlib is a C++ toolkit containing machine learning algorithms and tools for creating complex software in C++ to solve real world problems.
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Neuroph is lightweight Java Neural Network Framework which can be used to develop common neural network architectures. Small number of basic classes which correspond to basic NN concepts, and GUI editor makes it easy to learn and use.
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MLX Engine
LM Studio Apple MLX engine
MLX Engine is the Apple MLX-based inference backend used by LM Studio to run large language models efficiently on Apple Silicon hardware. Built on top of the mlx-lm and mlx-vlm ecosystems, the engine provides a unified architecture capable of supporting both text-only and multimodal models. Its design focuses on high-performance on-device inference, leveraging Apple’s MLX stack to accelerate computation on M-series chips. The project introduces modular VisionAddOn components that allow image embeddings to be integrated seamlessly into language model workflows. It is bundled with newer versions of LM Studio but can also be used independently for experimentation and development. Overall, mlx-engine serves as a specialized high-efficiency runtime for local AI workloads on macOS systems. -
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Generative Models
Collection of generative models, e.g. GAN, VAE in Pytorch
This project is a comprehensive open-source collection of implementations of various generative machine learning models designed to help researchers and developers experiment with deep generative techniques. The repository contains practical implementations of well-known architectures such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), Restricted Boltzmann Machines, and Helmholtz Machines, implemented primarily using modern deep learning frameworks like PyTorch and TensorFlow. These models are widely used in artificial intelligence to generate new data that resembles the training data, such as images, text, or other structured outputs. The repository serves as an educational and experimental environment where users can study how generative models work internally and replicate results from academic research papers. -
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CVPR 2026
Collection of CVPR 2026 Papers and Open Source Projects
CVPR2026-Papers-with-Code is a community-maintained repository that collects research papers and corresponding open-source implementations from the CVPR 2026 conference and related computer vision research. The repository acts as a continuously updated catalog of cutting-edge research across a wide range of topics including computer vision, multimodal AI, generative models, diffusion systems, autonomous driving, medical imaging, and remote sensing. Each entry typically links to the research paper as well as the public code repository associated with the work, allowing researchers and developers to quickly access reproducible implementations. The project serves as a centralized index that makes it easier for practitioners to explore the latest advances presented at major computer vision conferences. In addition to the current CVPR cycle, the repository also references related lists covering earlier conferences such as ECCV and ICCV, creating a broader archive of vision research. -
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AIGC-Interview-Book
AIGC algorithm engineer interview secrets
AIGC-Interview-Book is a large educational repository designed to help engineers prepare for technical interviews related to artificial intelligence and generative AI roles. The project compiles knowledge from industry practitioners and researchers into a structured reference covering the AI ecosystem. Topics included in the repository span large language models, generative AI systems, traditional deep learning methods, reinforcement learning, computer vision, natural language processing, and machine learning theory. In addition to technical concepts, the repository also contains interview preparation materials such as practice questions, hiring insights, and career advice for AI engineers. The materials are organized so readers can study fundamental topics as well as advanced research areas that frequently appear in technical interviews. -
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AI-Tutorials/Implementations Notebooks
Codes/Notebooks for AI Projects
AI-Tutorials/Implementations Notebooks repository is a comprehensive collection of artificial intelligence tutorials and implementation examples intended for developers, students, and researchers who want to learn by building practical AI projects. The repository contains numerous Jupyter notebooks and code samples that demonstrate modern techniques in machine learning, deep learning, data science, and large language model workflows. It includes implementations for a wide range of AI topics such as computer vision, agent systems, federated learning, distributed systems, adversarial attacks, and generative AI. Many of the tutorials focus on building AI agents, multi-agent systems, and workflows that integrate language models with external tools or APIs. The codebase acts as a hands-on learning resource, allowing users to experiment with new frameworks, architectures, and machine learning workflows through guided examples. -
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Watermark-Removal
Machine learning image inpainting task that removes watermarks
Watermark-Removal repository is a machine learning project focused on removing visible watermarks from digital images using deep learning and image inpainting techniques. The system analyzes an image containing a watermark and attempts to reconstruct the underlying visual content so that the watermark is removed while preserving the original appearance of the image. The project uses neural network models inspired by research in contextual attention and gated convolution, which are methods commonly applied to image restoration tasks. Through these techniques, the model learns to identify regions of the image affected by the watermark and generate realistic replacements for the missing visual information. The repository contains code for preprocessing images, training the model, and running inference on images to automatically remove watermark artifacts. -
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CVPR 2025
Collection of CVPR 2025 papers and open source projects
CVPR 2025 curates accepted CVPR 2025 papers and pairs them with their corresponding code implementations when available, giving researchers and practitioners a fast way to move from reading to reproducing. It organizes entries by topic areas such as detection, segmentation, generative models, 3D vision, multi-modal learning, and efficiency, so you can navigate the year’s output efficiently. Each paper entry typically includes a title, author list, and links to the paper PDF and official or third-party code repositories. The list frequently highlights benchmarks, leaderboards, or notable results so readers can assess impact at a glance. Because conference content evolves rapidly, the repository is updated as authors release code or refine readme instructions, keeping the collection timely. For teams planning literature reviews, study groups, or rapid prototyping sprints, it acts as a central index to the year’s most relevant methods with working implementations. -
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DeepDanbooru
AI based multi-label girl image classification system
DeepDanbooru is a deep learning system designed to automatically tag anime-style images using neural networks trained on datasets derived from the Danbooru imageboard. The project focuses on multi-label image classification, where a model predicts multiple descriptive tags that represent visual elements in an image. These tags may include characters, styles, clothing, emotions, or other attributes associated with anime artwork. The system uses convolutional neural networks trained on large datasets of tagged images to learn relationships between visual features and textual labels. Because the Danbooru dataset contains millions of images with extensive annotations, it provides a valuable training resource for machine learning models specializing in illustration analysis. Such datasets have been widely used for tasks including automatic image tagging, anime face detection, and generative modeling research. -
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Deepnote
Deepnote is a drop-in replacement for Jupyter
Deepnote is an open-source collaborative data science notebook platform designed as a modern alternative to traditional Jupyter notebooks. The project provides an AI-first computational environment where users can write, analyze, and share code, data, and visualizations in a single integrated workspace. Built on top of the Jupyter kernel ecosystem, it maintains compatibility with existing notebook workflows while introducing additional features focused on collaboration and automation. The system supports programming languages such as Python, R, and SQL and allows users to execute and analyze data directly within interactive notebooks. Deepnote emphasizes team-based data science by enabling real-time collaboration similar to shared document editors, allowing multiple users to work simultaneously on the same notebook environment. -
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LibrePhotos
A self-hosted open source photo management service
LibrePhotos is an open-source self-hosted photo management platform designed to organize, browse, and analyze personal media libraries while preserving user privacy. The system allows individuals to store and manage their photos and videos locally rather than relying on commercial cloud services. It provides features similar to services like Google Photos but runs on a private server controlled by the user. The application includes AI-powered tools that automatically analyze images to detect faces, objects, and locations, allowing photos to be grouped and searched more efficiently. LibrePhotos supports a wide variety of media formats and provides a web interface that can be accessed from different devices and operating systems. The platform is built using a Django backend and a React frontend, forming a full-stack web application architecture. -
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MLflow
Open source platform for the machine learning lifecycle
MLflow is a platform to streamline machine learning development, including tracking experiments, packaging code into reproducible runs, and sharing and deploying models. MLflow offers a set of lightweight APIs that can be used with any existing machine learning application or library (TensorFlow, PyTorch, XGBoost, etc), wherever you currently run ML code (e.g. in notebooks, standalone applications or the cloud). -
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Speech recognition research toolkit
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BitNet
BitNet: Scaling 1-bit Transformers for Large Language Models
BitNet is a machine learning research implementation that explores extremely low-precision neural network architectures designed to dramatically reduce the computational cost of large language models. The project implements the BitNet architecture described in research on scaling transformer models using extremely low-bit quantization techniques. In this approach, neural network weights are quantized to approximately one bit per parameter, allowing models to operate with far lower memory usage than traditional 16-bit or 32-bit neural networks. The architecture introduces specialized layers such as BitLinear, which replace standard linear projections in transformer networks with quantized operations. By limiting weight precision while maintaining efficient scaling and normalization strategies, the architecture aims to retain competitive performance while significantly reducing hardware requirements. -
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CodeSearchNet
Datasets, tools, and benchmarks for representation learning of code
CodeSearchNet is a large-scale dataset and research benchmark designed to advance the development of systems that retrieve source code using natural language queries. The project was created through collaboration between GitHub and Microsoft Research and aims to support research on semantic code search and program understanding. The dataset contains millions of pairs of source code functions and corresponding documentation comments extracted from open-source repositories. These pairs allow machine learning models to learn relationships between natural language descriptions and programming code. The dataset currently covers several widely used programming languages, including Python, JavaScript, Ruby, Go, Java, and PHP. In addition to the dataset itself, the repository includes baseline models, evaluation tools, and instructions for building code retrieval systems that can map user queries to relevant code snippets. -
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Fashion-MNIST
A MNIST-like fashion product database
Fashion-MNIST is an open-source dataset created by Zalando Research that provides a standardized benchmark for image classification algorithms in machine learning. The dataset contains grayscale images of fashion products such as shirts, shoes, coats, and bags, each labeled according to its clothing category. It was designed as a direct replacement for the original MNIST handwritten digits dataset, maintaining the same structure and image size so that researchers could easily switch datasets without modifying their experimental pipelines. The dataset consists of 70,000 images in total, with 60,000 examples used for training and 10,000 reserved for testing. Each image has a resolution of 28 by 28 pixels and belongs to one of ten clothing classes, making it suitable for evaluating classification models. Because the dataset represents real-world objects rather than handwritten digits, it offers a more challenging benchmark for testing machine learning algorithms. -
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NSFW Data Scraper
Collection of scripts to aggregate image data
NSFW Data Scraper is an open-source project that provides scripts for automatically collecting large datasets of images intended for training NSFW image classification systems. The repository focuses on aggregating image data from various online sources so that developers can build datasets suitable for training content moderation models. These datasets typically contain images categorized into different classes associated with adult or explicit content, which can then be used to train neural networks that detect unsafe or inappropriate material. The scripts automate the process of downloading and organizing large volumes of images, significantly reducing the manual effort required to build training datasets. The project was originally created to support research and development of machine learning models capable of identifying explicit or sensitive visual content. -
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Teachable Machine
Explore how machine learning works, live in the browser
Teachable Machine is the open-source implementation of an experimental machine learning tool created by Google Creative Lab that allows users to train simple machine learning models directly in a web browser. The project demonstrates how neural networks can be trained interactively using images captured from a webcam or other inputs without requiring programming knowledge. Users can provide example images for different categories, and the system trains a model that learns to classify those inputs in real time. The project is built using web technologies and the TensorFlow.js ecosystem, enabling machine learning models to run locally within the browser environment. Because the training occurs locally, the system can respond quickly to new examples and provide immediate feedback to users. -
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TorchCode
Practice implementing softmax, attention, GPT-2 and more
TorchCode is an interactive learning and practice platform designed to help developers master PyTorch by implementing core machine learning operations and architectures from scratch. It is structured similarly to competitive programming platforms like LeetCode but focuses specifically on tensor operations and deep learning concepts. The platform provides a collection of curated problems that cover fundamental topics such as activation functions, normalization layers, attention mechanisms, and full transformer architectures. It runs in a Jupyter-based environment, allowing users to write, test, and debug their code interactively while receiving immediate feedback. An automated judging system evaluates correctness, gradient flow, and numerical stability, helping users understand both functional and theoretical aspects of their implementations. -
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Zero to Mastery Machine Learning
All course materials for the Zero to Mastery Machine Learning
Zero to Mastery Machine Learning is an open-source repository that contains the complete course materials for the Zero to Mastery Machine Learning and Data Science bootcamp. The project provides a structured curriculum designed to teach machine learning and data science using Python through hands-on projects and interactive notebooks. The repository includes datasets, Jupyter notebooks, documentation, and example code that walk learners through the entire machine learning workflow from problem definition to model deployment. The course introduces essential tools such as NumPy, pandas, Matplotlib, and scikit-learn before moving on to deep learning with frameworks like TensorFlow and Keras. It also includes milestone projects that demonstrate how to build end-to-end machine learning systems using real datasets, including classification and regression tasks. -
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face.evoLVe
High-Performance Face Recognition Library on PaddlePaddle & PyTorch
face.evoLVe is a high-performance face recognition library designed for research and real-world applications in computer vision. The project provides a comprehensive framework for building and training modern face recognition models using deep learning architectures. It includes components for face alignment, landmark localization, data preprocessing, and model training pipelines that allow developers to construct end-to-end facial recognition systems. The repository supports multiple neural network backbones such as ResNet, DenseNet, MobileNet, and ShuffleNet, enabling experimentation with different architectures depending on performance requirements. It also implements a wide range of loss functions commonly used in face recognition research, including ArcFace, CosFace, Triplet loss, and Softmax variants. To improve scalability, the library introduces distributed training techniques that allow large models to be trained efficiently across multiple GPUs.
