Search Results for "classification"
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262 projects for "classification" with 1 filter applied:
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Build Agents and Models on One PlatformGemini Enterprise Agent Platform is Google Cloud's comprehensive platform for developers to build, scale, govern, and optimize agents and models. Choose from Google's most advanced models and third-party models like Anthropic's Claude Model Family.
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Stop Cyber Threats with VM-Series Next-Gen Firewall on AzureGain integrated visibility across all traffic in a single pass. Deploy Palo Alto Networks VM-Series to determine application identity and content while automating security policy updates via rich APIs.
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pyAudioAnalysis
Python Audio Analysis Library: Feature Extraction, Classification
pyAudioAnalysis is an open-source Python library designed for audio signal analysis, machine learning, and music information retrieval tasks. The project provides a collection of tools that allow developers to extract meaningful features from audio files and use those features for classification, segmentation, and analysis. The library supports multiple audio processing workflows, including feature extraction from raw audio signals, training of machine learning models, and automatic audio segmentation. It also includes utilities for visualizing audio features and analyzing patterns within sound recordings, which can be useful in applications such as speech recognition, music classification, and acoustic event detection. ... -
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DINOv3
Reference PyTorch implementation and models for DINOv3
...The model supports multiple backbone architectures, including Vision Transformers (ViT), and can handle larger image resolutions with improved stability during training. The learned embeddings generalize robustly across tasks like classification, retrieval, and segmentation without fine-tuning, showing state-of-the-art transfer performance among self-supervised models. -
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MobileCLIP
Implementation of "MobileCLIP" CVPR 2024
MobileCLIP is a family of efficient image-text embedding models designed for real-time, on-device retrieval and zero-shot classification. The repo provides training, inference, and evaluation code for MobileCLIP models trained on DataCompDR, and for newer MobileCLIP2 models trained on DFNDR. It includes an iOS demo app and Core ML artifacts to showcase practical, offline photo search and classification on iPhone-class hardware. Project notes highlight latency/accuracy trade-offs, with MobileCLIP2 variants matching or surpassing larger baselines at notably lower parameter counts and runtime on mobile devices. ... -
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CLIP
CLIP, Predict the most relevant text snippet given an image
CLIP (Contrastive Language-Image Pretraining) is a neural model that links images and text in a shared embedding space, allowing zero-shot image classification, similarity search, and multimodal alignment. It was trained on large sets of (image, caption) pairs using a contrastive objective: images and their matching text are pulled together in embedding space, while mismatches are pushed apart. Once trained, you can give it any text labels and ask it to pick which label best matches a given image—even without explicit training for that classification task. ... -
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Tally
Let agents classify your bank transactions
Tally is an open-source, AI-assisted tool designed to automate the classification of personal financial transactions, helping users turn raw bank data into meaningful categories without manual tagging. At its core, Tally pairs a local rule engine with large language models so that an AI assistant (like Claude Code, Copilot, or any CLI agent) interprets, suggests, and categorizes expenses, savings, subscriptions, and income events based on your own rules and behavior. -
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OpenAI Privacy Filter
Bidirectional token-classification model for identifiable info
OpenAI Privacy Filter is an open-weight machine learning model designed to detect and mask personally identifiable information in text with high efficiency and contextual awareness. It operates as a bidirectional token classification system that labels sensitive data in a single forward pass rather than generating text sequentially, enabling fast processing for large datasets. The model supports long-context inputs, allowing it to analyze extensive documents without chunking, which improves consistency in redaction tasks. It can run locally on standard hardware, ensuring that sensitive information never leaves the user’s environment and supporting privacy-first workflows. ... -
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AutoTrain Advanced
Faster and easier training and deployments
...The project provides a no-code and low-code interface that allows users to train models using custom datasets without needing extensive expertise in machine learning engineering. It supports a wide range of tasks including text classification, sequence-to-sequence modeling, token classification, sentence embedding training, and large language model fine-tuning. The system integrates closely with the Hugging Face ecosystem and allows developers to train models using datasets hosted on the Hugging Face Hub. AutoTrain Advanced can run locally or in cloud environments, making it adaptable to different computational setups. ... -
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GeoIP
This project automatically generates GeoIP files in multiple formats
...Rather than depending solely on the official GeoLite2 data, geoip augments and merges data sources (especially for certain regions) to improve coverage or tailor by use-case (e.g. proxy-specific rules, private networks, or region-based classification). The repo provides automated, periodic releases (e.g. weekly or on schedule) and also offers a CLI tool so users can regenerate or customize geo data in the format they need — for example, producing a .dat file for V2Ray / Xray-core, or a MaxMind-compatible .mmdb. -
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Whisper
Robust Speech Recognition via Large-Scale Weak Supervision
...These tasks are jointly represented as a sequence of tokens to be predicted by the decoder, allowing a single model to replace many stages of a traditional speech-processing pipeline. The multitask training format uses a set of special tokens that serve as task specifiers or classification targets. -
MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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GeoAI
GeoAI: Artificial Intelligence for Geospatial Data
...It provides a unified framework that combines machine learning libraries such as PyTorch and Transformers with geospatial tools, allowing users to process satellite imagery, aerial photos, and vector datasets in a streamlined workflow. The platform supports a wide range of tasks including image classification, object detection, segmentation, and change detection, making it suitable for applications in environmental monitoring, urban planning, and disaster response. GeoAI simplifies complex workflows by offering high-level APIs that abstract data preprocessing, model training, and inference, reducing the technical barrier for users who are not experts in both AI and geospatial systems. -
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Vision Transformer Pytorch
Implementation of Vision Transformer, a simple way to achieve SOTA
This repository provides a from-scratch, minimalist implementation of the Vision Transformer (ViT) in PyTorch, focusing on the core architectural pieces needed for image classification. It breaks down the model into patch embedding, positional encoding, multi-head self-attention, feed-forward blocks, and a classification head so you can understand each component in isolation. The code is intentionally compact and modular, which makes it easy to tinker with hyperparameters, depth, width, and attention dimensions. ... -
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DINOv2
PyTorch code and models for the DINOv2 self-supervised learning
...It builds on the DINO idea of student–teacher distillation and adapts it to modern Vision Transformer backbones with a carefully tuned recipe for data augmentation, optimization, and multi-crop training. The core promise is that a single pretrained backbone can transfer well to many downstream tasks—from linear probing on classification to retrieval, detection, and segmentation—often requiring little or no fine-tuning. The repository includes code for training, evaluating, and feature extraction, with utilities to run k-NN or linear evaluation baselines to assess representation quality. Pretrained checkpoints cover multiple model sizes so practitioners can trade accuracy for speed and memory depending on their deployment constraints. -
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TabPFN
Foundation Model for Tabular Data
TabPFN is an open-source machine learning system that introduces a foundation model designed specifically for tabular data analysis. The model is based on transformer architectures and implements a prior-data fitted network that can perform supervised learning tasks such as classification and regression with minimal configuration. Unlike many traditional machine learning workflows that require extensive hyperparameter tuning and training cycles, TabPFN is pre-trained to perform inference directly on tabular datasets. This allows it to generate predictions extremely quickly, often within seconds, while maintaining competitive accuracy on small and medium-sized datasets. ... -
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Kimi-Audio
Audio foundation model excelling in audio understanding
Kimi-Audio is an ambitious open-source audio foundation model designed to unify a wide array of audio processing tasks — from speech recognition and audio understanding to generative conversation and sound event classification — within a single cohesive architecture. Instead of fragmenting work across specialized models, Kimi-Audio handles automatic speech recognition (ASR), audio question answering, automatic audio captioning, speech emotion recognition, and audio-to-text chat in one system, enabling developers to build rich, multimodal audio applications without stitching together disparate components. ... -
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machine-learning-refined
Master the fundamentals of machine learning, deep learning
...Instead of presenting algorithms purely through mathematical derivations, the repository emphasizes geometric intuition, visualization, and step-by-step experimentation. It includes Jupyter notebooks and scripts that illustrate core machine learning topics such as regression, classification, optimization methods, and neural networks. These materials allow learners to see how algorithms behave during training and how different parameters affect model performance. -
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Zero to Mastery Machine Learning
All course materials for the Zero to Mastery Machine Learning
...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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Machine Learning Zoomcamp
Learn ML engineering for free in 4 months
...The project is designed to guide learners through the complete lifecycle of developing machine learning systems, starting with data preparation and model training and ending with production deployment. Participants learn how to build regression and classification models using Python libraries such as NumPy, Pandas, and Scikit-learn. The course also introduces more advanced topics including decision trees, ensemble methods, and neural networks. Later modules focus on practical engineering topics such as containerization with Docker, API development with FastAPI, and scaling machine learning services using Kubernetes and cloud platforms. ... -
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ZeroGPU Router
Cut inference costs without dumbing down your agent
...It is designed to reduce inference costs without replacing the main reasoning model used by the agent. The router exposes tools for tasks such as summarization, classification, PII redaction, entity extraction, JSON extraction, follow-up generation, and short chat. It works through MCP for OpenClaw and through a CLI plus plugin workflow for Claude Code. Each routed call can report useful metadata such as the selected model, latency, and estimated savings. Its main value is letting agents reserve expensive frontier models for complex reasoning while offloading routine language tasks to cheaper specialized routes. -
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Paperless-AI
AI-powered document analysis and tagging for Paperless-ngx
Paperless-AI is an AI-powered extension designed to enhance document management within Paperless-ngx by automating analysis, classification, and organization tasks. It continuously monitors incoming documents and processes them using various AI backends, enabling automatic assignment of titles, tags, document types, and correspondents. It integrates with multiple OpenAI-compatible services as well as local models, giving users flexibility in how document intelligence is handled. ... -
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Claude Cookbooks
A collection of notebooks/recipes showcasing ways of using Claude
Claude Cookbooks is a curated collection of practical examples, notebooks, and implementation guides that demonstrate how to effectively use Claude’s API across a wide range of tasks. It serves as both a learning resource and a reference library, helping developers understand how to apply AI capabilities such as classification, summarization, and retrieval-augmented generation in real-world scenarios. The repository includes structured examples for integrating Claude with external tools, databases, and APIs, showcasing how to extend its functionality beyond basic text generation. It also covers advanced techniques like sub-agent orchestration, prompt optimization, and automated evaluation workflows. ... -
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Ultralytics
Ultralytics YOLO
Ultralytics is a comprehensive computer vision framework that provides state-of-the-art implementations of the YOLO (You Only Look Once) family of models, enabling developers to perform tasks such as object detection, segmentation, classification, tracking, and pose estimation within a unified system. It is designed to be fast, accurate, and easy to use, offering both command-line and Python-based interfaces for training, validation, and deployment of machine learning models. The framework supports a full end-to-end workflow, including dataset preparation, model training, evaluation, and export to various deployment formats. ... -
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model2Vec
Fast State-of-the-Art Static Embeddings
...By using a distillation-based approach, it can produce lightweight models that run efficiently on CPUs, making it suitable for edge applications and large-scale processing pipelines. The resulting models can be used for a wide range of tasks, including semantic search, clustering, classification, and retrieval-augmented generation systems. One of its key advantages is its simplicity, as it requires minimal dependencies and can generate embeddings extremely quickly compared to traditional transformer-based approaches. -
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NLP
Open source NLP guide with models, methods, and real use cases
...It also introduces topic modeling with LDA, keyword extraction techniques, and document similarity methods. NLP extends into real-world applications, including sentiment analysis and text classification, helping readers connect concepts to use cases. Designed for accessibility, the project evolves over time, allowing updates and improvements as NLP techniques advance. It reflects a practical approach to learning, where readers can explore code, experiment with models, and build foundational skills in machine learning-driven language processing. -
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SimpleHTR
Handwritten Text Recognition (HTR) system implemented with TensorFlow
...The system uses a combination of convolutional neural networks and recurrent neural networks to extract visual features and model sequential character patterns in handwriting. It also employs connectionist temporal classification (CTC) to align predicted character sequences with input images without requiring character-level segmentation. The repository provides code for training models, performing inference on handwritten text images, and evaluating recognition accuracy. SimpleHTR is commonly used as an educational example for understanding how modern handwriting recognition systems operate. -
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Natural Language Toolkit
NLTK Source
...The toolkit includes implementations of many foundational NLP algorithms and utilities, enabling developers to perform tasks such as tokenization, stemming, parsing, classification, and semantic reasoning. NLTK was originally developed to support research and teaching in computational linguistics and artificial intelligence, and it has become one of the most influential educational platforms for learning NLP in Python. The project also includes access to numerous linguistic corpora and lexical resources that can be downloaded and used directly in experiments and applications.
