Search Results for "classification" - Page 6
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Showing 644 open source projects for "classification"
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Tribuo
Tribuo - A Java machine learning library
Tribuo* is a machine learning library written in Java. It provides tools for classification, regression, clustering, model development, and more. It provides a unified interface to many popular third-party ML libraries like xgboost and liblinear. With interfaces to native code, Tribuo also makes it possible to deploy models trained by Python libraries (e.g. scikit-learn, and pytorch) in a Java program. Tribuo is licensed under Apache 2.0. -
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tsfresh
Automatic extraction of relevant features from time series
...The extracted features can be used to describe or cluster time series based on the extracted characteristics. Further, they can be used to build models that perform classification/regression tasks on the time series. Often the features give new insights into time series and their dynamics. -
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hCaptcha Challenger
Gracefully face hCaptcha challenge with multimodal llms
hCaptcha Challenger is an open-source automation framework designed to solve hCaptcha verification challenges using computer vision models and multimodal reasoning techniques. The project integrates machine learning models capable of analyzing visual captcha tasks and identifying the correct responses required to pass the verification process. Instead of relying on third-party captcha-solving services or browser scripts, the system operates independently by using pretrained neural networks... -
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Qwen3 Embedding
Designed for text embedding and ranking tasks
...It achieves state-of-the-art performance on benchmarks like MTEB (Multilingual Text Embedding Benchmark) and supports instruction-aware embedding (i.e. embedding task instructions along with queries) and flexible embedding/vector dimension definitions. It is meant for tasks such as text retrieval, classification, clustering, bitext mining, and code retrieval. -
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NannyML
Detecting silent model failure. NannyML estimates performance
...Built for data scientists, NannyML has an easy-to-use interface, and interactive visualizations, is completely model-agnostic, and currently supports all tabular classification use cases. NannyML closes the loop with performance monitoring and post deployment data science, empowering data scientist to quickly understand and automatically detect silent model failure. By using NannyML, data scientists can finally maintain complete visibility and trust in their deployed machine learning models. When the actual outcome of your deployed prediction models is delayed, or even when post-deployment target labels are completely absent, you can use NannyML's CBPE-algorithm to estimate model performance. -
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Pytorch-toolbelt
PyTorch extensions for fast R&D prototyping and Kaggle farming
...Easy model building using flexible encoder-decoder architecture. Modules: CoordConv, SCSE, Hypercolumn, Depthwise separable convolution and more. GPU-friendly test-time augmentation TTA for segmentation and classification. GPU-friendly inference on huge (5000x5000) images. Every-day common routines (fix/restore random seed, filesystem utils, metrics). Losses: BinaryFocalLoss, Focal, ReducedFocal, Lovasz, Jaccard and Dice losses, Wing Loss and more. Extras for Catalyst library (Visualization of batch predictions, additional metrics). By design, both encoder and decoder produces a list of tensors, from fine (high-resolution, indexed 0) to coarse (low-resolution) feature maps. ... -
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Raster Vision
Open source framework for deep learning satellite and aerial imagery
Raster Vision is an open source framework for Python developers building computer vision models on satellite, aerial, and other large imagery sets (including oblique drone imagery). There is built-in support for chip classification, object detection, and semantic segmentation using PyTorch. Raster Vision allows engineers to quickly and repeatably configure pipelines that go through core components of a machine learning workflow: analyzing training data, creating training chips, training models, creating predictions, evaluating models, and bundling the model files and configuration for easy deployment. ... -
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FLAML
A fast library for AutoML and tuning
FLAML is a lightweight Python library that finds accurate machine learning models automatically, efficiently and economically. It frees users from selecting learners and hyperparameters for each learner. For common machine learning tasks like classification and regression, it quickly finds quality models for user-provided data with low computational resources. It supports both classical machine learning models and deep neural networks. It is easy to customize or extend. Users can find their desired customizability from a smooth range: minimal customization (computational resource budget), medium customization (e.g., scikit-style learner, search space, and metric), or full customization (arbitrary training and evaluation code). ... -
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DeepPavlov
A library for deep learning end-to-end dialog systems and chatbots
...It has comprehensive and flexible tools that let developers and NLP researchers create production-ready conversational skills and complex multi-skill conversational assistants. Use BERT and other state-of-the-art deep learning models to solve classification, NER, Q&A and other NLP tasks. DeepPavlov Agent allows building industrial solutions with multi-skill integration via API services. -
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natural
General natural language facilities for node
"Natural" is a general natural language facility for nodejs. It offers a broad range of functionalities for natural language processing. Tokenizing, stemming, classification, phonetics, tf-idf, WordNet, string similarity, and some inflections are currently supported. It’s still in the early stages, so we’re very interested in bug reports, contributions and the like. Note that many algorithms from Rob Ellis’s node-nltools are being merged into this project and will be maintained from here onward. ... -
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Computer Vision in Action
A computer vision closed-loop learning platform
...It serves as a hands-on companion for learners and engineers who want to understand not just the theory, but how computer vision is actually implemented for tasks like object detection, image classification, feature tracking, optical flow, and image segmentation. The repository includes structured code examples, scripts, and notebooks that cover pipeline construction, preprocessing, model inference, and visual output rendering, making it easy for newcomers or intermediate practitioners to adapt patterns to their own projects. It also explores how to combine classical computer vision techniques with modern neural network-based models, offering insight into when each approach is most effective. -
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ktrain
ktrain is a Python library that makes deep learning AI more accessible
ktrain is a Python library that makes deep learning and AI more accessible and easier to apply. ktrain is a lightweight wrapper for the deep learning library TensorFlow Keras (and other libraries) to help build, train, and deploy neural networks and other machine learning models. Inspired by ML framework extensions like fastai and ludwig, ktrain is designed to make deep learning and AI more accessible and easier to apply for both newcomers and experienced practitioners. With only a few lines... -
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LIBSVM.jl
LIBSVM bindings for Julia
LIBSVM bindings for Julia. This is a Julia interface for LIBSVM and for the linear SVM model provided by LIBLINEAR. Supports all LIBSVM models: classification C-SVC, nu-SVC, regression: epsilon-SVR, nu-SVR and distribution estimation: one-class SVM. Model objects are represented by Julia-type SVM which gives you easy access to model features and can be saved e.g. as JLD file. -
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Weka
Machine learning software to solve data mining problems
Weka is a collection of machine learning algorithms for solving real-world data mining problems. It is written in Java and runs on almost any platform. The algorithms can either be applied directly to a dataset or called from your own Java code. -
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audioFlux
A library for audio and music analysis, feature extraction
...It supports dozens of time-frequency analysis transformation methods and hundreds of corresponding time-domain and frequency-domain feature combinations. It can be provided to deep learning networks for training and is used to study various tasks in the audio field such as Classification, Separation, Music Information Retrieval(MIR) ASR, etc. -
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OpenPromptStudio
Visual editor for AI prompts with translation, categories, and tools
...It focuses on improving the workflow for building prompts by turning them into structured, visual components that are easier to edit and rearrange. It supports the creation and classification of prompt segments, allowing users to organize them into different types such as styles, quality modifiers, commands, or general prompt elements. OpenPromptStudio also provides translation capabilities that can convert Chinese prompts into English and display Chinese translations for English prompts, which is especially useful for tools that require English inputs. ... -
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InternVL
A Pioneering Open-Source Alternative to GPT-4o
...InternVL is trained on massive collections of image-text data, enabling it to learn representations that capture both visual patterns and semantic meaning. The model supports a wide variety of tasks, including visual perception, image classification, and cross-modal retrieval between images and text. It can also be connected to language models to enable conversational interfaces that understand images, videos, and other visual content. By combining large-scale vision architectures with language reasoning capabilities, the project aims to create a more general multimodal AI system capable of handling diverse real-world tasks. -
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Taxallnomy is a taxonomic database that provides taxonomic lineages according to the ranks used on Linnean classification system (e.g. Kingdom, Phylum, Class etc.). Taxallnomy is based on the NCBI Taxonomy, thus you will find along the taxonomic lineage either taxa originally ranked in NCBI Taxonomy or some unique nodes created by the Taxallnomy algorithm, since some taxonomic ranks are missing in the original taxonomic lineage (for example, the Superclass rank is missing on the Homo sapiens lineage).
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SAGA - System for Automated Geoscientific Analyses - is a Geographic Information System (GIS) software with immense capabilities for geodata processing and analysis. SAGA is programmed in the object oriented C++ language and supports the implementation of new functions with a very effective Application Programming Interface (API). Functions are organised as modules in framework independent Module Libraries and can be accessed via SAGA’s Graphical User Interface (GUI) or various scripting...
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A SOA framework for web content classification, clustering and automated interlinking of terms between documents. Will provide an expandable set of services such as semantic search, ranking, retrieval and classification of large scale web resources.
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hookprobe
Free AI that blocks hackers while you sleep. Runs on cheap hardware
HookProbe is an open-source AI-native intrusion detection system (IDS/IPS) that runs on Raspberry Pi and edge devices. It combines eBPF/XDP kernel-level packet filtering with machine learning threat classification to deliver autonomous network security with zero cloud dependency. The stack includes NAPSE (AI packet inspection), HYDRA (threat intelligence pipeline), SENTINEL (ML classification engine), and AEGIS (autonomous defense orchestrator). In production, a single Raspberry Pi 5 processes 11M+ security events, classifies 177K ML verdicts, and tracks 11,800+ attacker IPs — all autonomously. ... -
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GeoDMA
Geographic feature extraction and data mining
GeoDMA is a plugin for TerraView software, used for geographical data mining. With a single image, the user can perform segmentation, attributes extraction, normalization and classification. -
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MobileNetV2
SSD-based object detection model trained on Open Images V4
...It is based on an inverted residual structure that allows for faster computation and fewer parameters, making it ideal for real-time applications on resource-constrained devices. MobileNetV2 is commonly used for image classification, object detection, and other computer vision tasks, achieving high accuracy while maintaining a small memory footprint. It also supports TensorFlow Lite for mobile device deployment, ensuring that developers can leverage its performance on a wide range of platforms. -
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Zero to Mastery Deep Learning TensorFlow
All course materials for the Zero to Mastery Deep Learning with TF
...It is structured as a series of progressively complex Jupyter notebooks that emphasize writing and understanding code before diving into theory, reinforcing learning through repetition and application. The material covers core machine learning workflows including regression, classification, computer vision, natural language processing, and time series forecasting, allowing users to build a well-rounded understanding of modern AI tasks. It also integrates milestone projects that simulate real-world scenarios, helping users translate abstract concepts into deployable solutions. -
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TEXminer
Text Mining Classification for Texts in ASCII, Unicode and PDF Format.
...TEXminer allows Language Detection by Letter Frequency Analysis, finding important Words by Cooccurrence Analysis, Determination of Central Expressions, Thematic Text Classification (also Semantic Groups) Fingerprint Comparison and Word Frequency. Because TEXminer is not disigned to have a Reference Corpus, Thematic Model Statistics uses Language Models (lexicons) to have Background Knowledge about certain Languages (English, German, French, Spanish, Italian, Russian), which are derived from Decaleon Project. ...
