Search Results for "classification" - Page 9
Sort By:
Showing 773 open source projects for "classification"
View related business solutions-
Ship Agents FasterGemini Enterprise Agent Platform lets you rapidly build, scale, govern and optimize production-ready agents grounded in your organization's data. The platform enables developers to build custom or pre-built agents for virtually any use case. New customers get $300 in free credits.
-
Enterprise-grade ITSM, for every businessFreshservice is an intuitive, AI-powered platform that helps IT, operations, and business teams deliver exceptional service without the usual complexity. Automate repetitive tasks, resolve issues faster, and provide seamless support across the organization. From managing incidents and assets to driving smarter decisions, Freshservice makes it easy to stay efficient and scale with confidence.
-
1
Promptify
se GPT or other prompt based models to get structured output
...Instead of manually crafting prompts for each task, Promptify introduces a unified architecture that combines prompt templates, language model interfaces, and processing pipelines into a single framework. This approach allows developers to perform tasks such as text classification, named entity recognition, question answering, and information extraction using consistent prompt templates. The library supports integration with multiple large language model providers, enabling users to experiment with various models without changing their overall workflow. -
2
FastViT
This repository contains the official implementation of research
...The models use lightweight attention and carefully engineered blocks to minimize token mixing costs while preserving representation power. Training and inference recipes highlight straightforward integration into common vision tasks such as classification, detection, and segmentation. The codebase provides reference implementations and checkpoints that make it easy to evaluate or fine-tune on downstream datasets. In practice, FastViT offers drop-in backbones that reduce compute and memory pressure without exotic training tricks. -
3
pyts
A Python package for time series classification
pyts is a Python package dedicated to time series classification. It aims to make time series classification easily accessible by providing preprocessing and utility tools, and implementations of several time series classification algorithms. The package comes up with many unit tests and continuous integration ensures new code integration and backward compatibility. The package is distributed under the 3-clause BSD license. -
4
jbig2enc
JBIG2 Encoder
...JBIG2 encodes bi-level (1 bpp) images using a number of clever tricks to get better compression than G4. This encoder can: Generate JBIG2 files, or fragments for embedding in PDFs Generic region encoding Perform symbol extraction, classification and text region coding Perform refinement coding and, Compress multipage documents It uses the (Apache-ish licensed) Leptonica library: http://leptonica.com/ -
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.
-
5
OpenNMT-tf
Neural machine translation and sequence learning using TensorFlow
...While neural machine translation is the main target task, it has been designed to more generally support sequence-to-sequence mapping, sequence tagging, sequence classification, language modeling. Models are described with code to allow training custom architectures and overriding default behavior. For example, the following instance defines a sequence-to-sequence model with 2 concatenated input features, a self-attentional encoder, and an attentional RNN decoder sharing its input and output embeddings. ... -
6
Paper-with-Code-of-Wireless-comm
Paper-with-Code-of-Wireless-communication-Based-on-DL
...Wireless communication research has increasingly adopted deep learning techniques to address complex tasks such as channel estimation, resource allocation, signal detection, and modulation classification. However, many academic publications do not release source code, which makes it difficult for new researchers to reproduce results or experiment with the proposed methods. This repository addresses that challenge by organizing a large set of papers and linking them to available implementations and related research resources. -
7
SOD
An Embedded Computer Vision & Machine Learning Library
SOD is an embedded, modern cross-platform computer vision and machine learning software library that expose a set of APIs for deep-learning, advanced media analysis & processing including real-time, multi-class object detection and model training on embedded systems with limited computational resource and IoT devices. SOD was built to provide a common infrastructure for computer vision applications and to accelerate the use of machine perception in open source as well as commercial products.... -
8
TXM
Unicode XML TEI text analysis platform
...rubrique61&lang=en TXM offers a comprehensive range of analysis tools (concordances, collocate search, frequency lists, etc.) based on the powerfull CQP full text search engine (http://cwb.sourceforge.net) and a range of statistical functions (factorial analysis, classification, cooccurrency analysis, etc.) based on R packages (http://www.r-project.org). Read the scientific background at the Textométrie project web site http://textometrie.ens-lyon.fr/?lang=en. Read a full description at the TEI Tools wiki http://wiki.tei-c.org/index.php/TXM. -
9
learn2learn
A PyTorch Library for Meta-learning Research
...It provides reusable components and meta-learning algorithms, making it easier to build, train, and evaluate models that can quickly adapt to new tasks with minimal data. Learn2Learn is widely used in research for tasks such as few-shot classification, reinforcement learning, and optimization. -
$300 Free Credits for Your Google Cloud ProjectsLaunch your next project with $300 in free Google Cloud credits—no strings attached. Test, build, and deploy without risk. Use your credits across the entire Google Cloud platform to find what works best for your needs. After your credits are used, continue with always-free tier services. Only pay when you're ready to scale. Sign up in minutes and start exploring.
-
10
Amplify
Automatic enrichment, enhancement, and explanation of your data
Amplify attaches afterburners to your data. Amplify explains metadata extraction, classification, tagging, and reporting. Eriches derivative data generation like thumbnails, previews, conversions, etc. Enhances batteries-included value-adds like data quality reports, image augmentation, OCR, translations, etc. Amplify leverages the decentralized compute provided by Bacalhau to magically enrich your data. A built-in suite of pipelines decides what your data is and how to best improve upon it. ... -
11
-
12
SentimentAnalysis-Rick&Morty
Rick & Morty Sentiment Analysis - End-of-Degree Project - UNIR
...Text mining is a fascinating application of data analysis that extracts relevant information from related writings in different linguistic contexts. And therefore, in natural language processing, sentiment analysis and classification stands out as a key application supported by text mining. Through the extraction of information from textual data, it becomes possible to identify and comprehend the sentiments and emotions conveyed. In this end-of-degree work, we analyze and classify the dialogue of characters in an English-language television series as "Rick and Morty" using Python. ... -
13
Transformers-Interpret
Model explainability that works seamlessly with Hugging Face
Transformers-Interpret is an interpretability tool for Transformer-based NLP models, providing insights into attention mechanisms and feature importance. -
14
ClassyVision
An end-to-end PyTorch framework for image and video classification
Classy Vision is a PyTorch-based framework designed for large-scale training and deployment of state-of-the-art image and video classification models. Developed by Facebook Research, it serves as an end-to-end system that simplifies the process of training at scale, reducing redundancy and friction in moving from research to production. Unlike traditional computer vision libraries that focus solely on modular components, Classy Vision provides a complete and unified framework, featuring distributed training, reproducible experiments, and flexible configuration tools. ... -
15
NanoDet-Plus
Lightweight anchor-free object detection model
Super fast and high accuracy lightweight anchor-free object detection model. Real-time on mobile devices. NanoDet is a FCOS-style one-stage anchor-free object detection model which using Generalized Focal Loss as classification and regression loss. In NanoDet-Plus, we propose a novel label assignment strategy with a simple assign guidance module (AGM) and a dynamic soft label assigner (DSLA) to solve the optimal label assignment problem in lightweight model training. We also introduce a light feature pyramid called Ghost-PAN to enhance multi-layer feature fusion. ... -
16
whiteboxgui
An interactive GUI for WhiteboxTools in a Jupyter-based environment
...Remote sensing and image processing tasks include image enhancement (e.g. panchromatic sharpening, contrast adjustments), image mosaicing, numerous filtering operations, simple classification (k-means), and common image transformations. WhiteboxTools also contains advanced tooling for spatial hydrological analysis (e.g. flow-accumulation, watershed delineation, stream network analysis, sink removal), terrain analysis (e.g. common terrain indices such as slope, curvatures, wetness index, hillshading; hypsometric analysis; etc. -
17
ACHE Focused Crawler
ACHE is a web crawler for domain-specific search
...ACHE differs from generic crawlers in sense that it uses page classifiers to distinguish between relevant and irrelevant pages in a given domain. A page classifier can be defined as a simple regular expression (e.g., that matches every page that contains a specific word) or a machine-learning-based classification model. ACHE also automatically learns how to prioritize links in order to efficiently locate relevant content while avoiding the retrieval of irrelevant pages. While ACHE was originally designed to perform focused crawls, it also supports other crawling tasks, including crawling all pages in a given web site and crawling Dark Web sites (using the TOR protocol). -
18
Text Classification
All kinds of text classification models and more with deep learning
Text Classification is a deep learning repository focused on text classification models for NLP. It provides a broad set of baseline architectures that can be used to study, train, compare, and adapt classification approaches. The project supports both single-label and multi-label classification, making it useful for sentence-level and document-level tasks. -
19
AiHound
AI powered image classification for nudity and documents / id-cards
AI Hound is designed to run from an USB pendrive or any other kind of removeable and writeable media. The programm checks all Office-documents, Images and videos for various categories for images. Actually It can recognice nudity/porn and scanned or photographed documents / ID- and credit-cards. I am working on a model that also recognice various types of drugs in images. -
20
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) and ASR etc. -
21
albert_zh
Implementation of A Lite Bert For Self-Supervised Learning Language
...The project includes several model variants, such as tiny, small, base, large, and xlarge-style releases, giving users options for speed, size, and accuracy tradeoffs. It also provides guidance for fine-tuning downstream tasks such as sentence-pair semantic similarity and Chinese classification benchmarks. The repository includes support paths for TensorFlow, PyTorch conversion, Keras loading, TensorFlow 2.0 loading, and TensorFlow Lite deployment for mobile scenarios. Overall, it is useful for Chinese NLP developers who need compact pretrained language models for classification, similarity, and other language understanding tasks. -
22
Mathematical Modelling Ontology
Ontology of types of mathematical models, and their properties
The Mathematical Modelling Ontology (MAMO) is a classification of the types of mathematical models used mostly in the life sciences, their variables, relationships and other relevant features. -
23
LSTMs for Human Activity Recognition
Human Activity Recognition example using TensorFlow on smartphone
LSTM-Human-Activity-Recognition is a machine learning project that demonstrates how recurrent neural networks can be used to recognize human activities from sensor data. The repository implements a deep learning model based on Long Short-Term Memory (LSTM) networks to classify physical activities using time-series data collected from wearable sensors. The project uses the well-known Human Activity Recognition dataset derived from smartphone accelerometer and gyroscope signals. Through the... -
24
learn-machine-learning-in-two-months
Essential Knowledge for learning Machine Learning in two months
...The project compiles curated resources, tutorials, and practical notebooks that introduce fundamental topics such as mathematics for machine learning, Python programming, and essential libraries like NumPy and TensorFlow. It progressively moves from foundational theory to more advanced subjects including regression, classification, neural networks, and model deployment. The repository emphasizes understanding the underlying principles of machine learning while also providing practical exercises and examples that allow learners to build and experiment with real models. Many sections include notebooks and code examples that demonstrate how algorithms are implemented and trained using modern machine learning frameworks. -
25
Python ML Jupyter Notebooks
Practice and tutorial-style notebooks
Python ML Jupyter Notebooks is an educational repository that demonstrates how to implement machine learning algorithms and data science workflows using Python. The project provides numerous examples and tutorials covering classical machine learning techniques such as regression, classification, clustering, and dimensionality reduction. It includes code implementations that show how to build models using popular libraries like scikit-learn, NumPy, pandas, and Matplotlib. The repository is designed to help learners understand both the theory and practical implementation of machine learning algorithms through step-by-step code examples. ...
