Search Results for "classification"
Sort By:
Showing 25 open source projects for "classification"
View related business solutions-
$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.
-
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.
-
1
LightGBM
Gradient boosting framework based on decision tree algorithms
...Parallel experiments have shown that LightGBM can attain linear speed-up through multiple machines for training in specific settings, all while consuming less memory. LightGBM supports parallel and GPU learning, and can handle large-scale data. It’s become widely-used for ranking, classification and many other machine learning tasks. -
2
dlib
Toolkit for making machine learning and data analysis applications
Dlib is a modern C++ toolkit containing machine learning algorithms and tools for creating complex software in C++ to solve real world problems. It is used in both industry and academia in a wide range of domains including robotics, embedded devices, mobile phones, and large high performance computing environments. Dlib's open source licensing allows you to use it in any application, free of charge. Good unit test coverage, the ratio of unit test lines of code to library lines of code is... -
3
CatBoost
High-performance library for gradient boosting on decision trees
CatBoost is a fast, high-performance open source library for gradient boosting on decision trees. It is a machine learning method with plenty of applications, including ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. CatBoost offers superior performance over other GBDT libraries on many datasets, and has several superb features. It has best in class prediction speed, supports both numerical and categorical features, has a fast and scalable GPU version, and readily comes with visualization tools. ... -
4
Simd Library
C++ image processing and machine learning library with using of SIMD
...It provides many useful high-performance algorithms for image processing such as pixel format conversion, image scaling and filtration, extraction of statistical information from images, motion detection, object detection and classification, neural networks. The algorithms are optimized with using of different SIMD CPU extensions. In particular, the library supports the following CPU extensions: SSE, AVX, AVX-512, and AMX for x86/x64, and NEON for ARM. The Simd Library has C API and also contains useful C++ classes and functions to facilitate access to C API. The library supports dynamic and static linking, 32-bit and 64-bit Windows and Linux, MSVS, G++ and Clang compilers, MSVS projects, and CMake build systems. -
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
DeepDetect
Deep Learning API and Server in C++14 support for Caffe, PyTorch
...While the Open Source Deep Learning Server is the core element, with REST API, and multi-platform support that allows training & inference everywhere, the Deep Learning Platform allows higher level management for training neural network models and using them as if they were simple code snippets. Ready for applications of image tagging, object detection, segmentation, OCR, Audio, Video, Text classification, CSV for tabular data and time series. Neural network templates for the most effective architectures for GPU, CPU, and Embedded devices. Training in a few hours and with small data thanks to 25+ pre-trained models. Full Open Source, with an ecosystem of tools (API clients, video, annotation, ...) Fast Server written in pure C++, a single codebase for Cloud, Desktop & Embedded. -
6
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. -
7
Hello AI World
Guide to deploying deep-learning inference networks
Hello AI World is a great way to start using Jetson and experiencing the power of AI. In just a couple of hours, you can have a set of deep learning inference demos up and running for realtime image classification and object detection on your Jetson Developer Kit with JetPack SDK and NVIDIA TensorRT. The tutorial focuses on networks related to computer vision, and includes the use of live cameras. You’ll also get to code your own easy-to-follow recognition program in Python or C++, and train your own DNN models onboard Jetson with PyTorch. ... -
8
Flashlight library
A C++ standalone library for machine learning
Flashlight is a fast, flexible machine learning library written entirely in C++ by Facebook AI Research and the creators of Torch, TensorFlow, Eigen, and Deep Speech. Native support in C++ and simple extensibility make Flashlight a powerful research framework that's hackable to its core and enables fast iteration on new experimental setups and algorithms with little unopinionated and without sacrificing performance. In a single repository, Flashlight provides apps for research across... -
9
Turi Create
Simplifies the development of custom machine learning models
Turi Create simplifies the development of custom machine learning models. You don't have to be a machine learning expert to add recommendations, object detection, image classification, image similarity or activity classification to your app. If you want your app to recognize specific objects in images, you can build your own model with just a few lines of code. Turi Create supports macOS 10.12+, Linux (with glibc 2.10+), Windows 10 (via WSL). Turi Create requires Python 2.7, 3.5, 3.6, 3.7, 3.8. Also, x86_64 architecture, and at least 4 GB of RAM. ... -
Secure File Transfer for Windows with Cerberus by RedwoodCerberus supports unlimited users and connections on a single IP, with built-in encryption, 2FA, and a browser-based web client — all deployable in under 15 minutes with a 25-day free trial.
-
10
Convolutional Recurrent Neural Network
Convolutional Recurrent Neural Network (CRNN) for image-based sequence
...This hybrid approach allows the model to recognize sequences of characters directly from images without requiring explicit character segmentation. The implementation also integrates the Connectionist Temporal Classification (CTC) loss function, enabling end-to-end training of the model using labeled sequence data. CRNN has been widely used in computer vision tasks that require interpreting text embedded in images, such as reading street signs, documents, or natural scene text. -
11
EasyPR
An easy, flexible, and accurate plate recognition project
...The system is designed to work in unconstrained environments, meaning it can handle images with varying lighting conditions, perspectives, and backgrounds. Its architecture includes multiple stages such as plate localization, character segmentation, and character classification to achieve accurate recognition results. -
12
This project aims to develop and share fast frequent subgraph mining and graph learning algorithms. Currently we release the frequent subgraph mining package FFSM and later we will include new functions for graph regression and classification package
-
13
libfastknn
Fast C++ KNN classifier
KNN Classifier library for C++, at background using armadillo. In k-NN classification, the output is a class membership. An object is classified by a majority vote of its neighbors, with the object being assigned to the class most common among its k nearest neighbors (k is a positive integer, typically small). If k = 1, then the object is simply assigned to the class of that single nearest neighbor. -
14
Scene is a computer vision framework that performs background subtraction and object tracking, using two traditional algorithms and three more recent algorithms based on neural networks and fuzzy classification rules. For each detected object, Scene sends TUIO messages to one or several client applications. The present release features GPU accelerated versions of all the background subtraction methods and morphological post processing of the object blobs with dilation and erosion filters, implemented in OpenCL. The framework was mainly designed as a toolkit for the rapid development of interactive art projects that explore dynamics of complex environments. ...
-
15
Adaptive Gaussian Filtering
Machine learning with Gaussian kernels.
Libagf is a machine learning library that includes adaptive kernel density estimators using Gaussian kernels and k-nearest neighbours. Operations include statistical classification, interpolation/non-linear regression and pdf estimation. For statistical classification there is a borders training feature for creating fast and general pre-trained models that nonetheless return the conditional probabilities. Libagf also includes clustering algorithms as well as comparison and validation routines. It is written in C++. -
16
CURRENNT
CUDA-enabled machine learning library for recurrent neural networks
CURRENNT is a machine learning library for Recurrent Neural Networks (RNNs) which uses NVIDIA graphics cards to accelerate the computations. The library implements uni- and bidirectional Long Short-Term Memory (LSTM) architectures and supports deep networks as well as very large data sets that do not fit into main memory. -
17
BudgetedSVM
BudgetedSVM: A C++ Toolbox for Large-scale, Non-linear Classification
We present BudgetedSVM, a C++ toolbox containing highly optimized implementations of three recently proposed algorithms for scalable training of Support Vector Machine (SVM) approximators: Adaptive Multi-hyperplane Machines (AMM), Budgeted Stochastic Gradient Descent (BSGD), and Low-rank Linearization SVM (LLSVM). BudgetedSVM trains models with accuracy comparable to LibSVM in time comparable to LibLinear, as it allows solving highly non-linear classi fication problems with millions of... -
18
ImageSorterBOW
Program for classification and sort images by contest.
Program for classification and sort images by contest. It is based on implementation OpenCV Bag of visual world method. -
19
Graphlet kernel framework
Calculates similarity between neighborhoods of two vertices in a graph
...The list of available similarity functions includes: cumulative random walk, standard random walk, standard graphlet kernel, edit distance graphlet kernel, label substitution graphlet kernel and edge indel graphlet kernel. The graphlet kernel framework can be used for vertex (node) classification in graphs, kernel-based clustering, or community detection. If you use this framework, please cite the following paper: Lugo-Martinez J, Radivojac P. Generalized graphlet kernels for probabilistic inference in sparse graphs. Network Science (2014) 2(2): 254-276. -
20
ECOC PAK is a C++ Library for the Error Correcting Output Codes classification framework. It supports several coding and decoding strategies as well as several classifiers.
-
21
mlpy
Machine Learning Python
mlpy is a Python module for Machine Learning built on top of NumPy/SciPy and of GSL. mlpy provides high-level functions and classes allowing, with few lines of code, the design of rich workflows for classification, regression, clustering and feature selection. mlpy is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License version 3. mlpy is available both for Python >=2.6 and Python 3.X. -
22
The data complexity library, DCoL, is a machine learning software that implements all metrics to characterize the apparent complexity of classification problems. The code is implemented in C++ and can be run on multiple platforms.
-
23
The name stands for ensemble learning framework. It is a collection of machine learning algorithms for classification and regression with the possibility of connecting them together via ensemble learning. It is written in C++.
-
24
BCAR is a library for the associative classification, which denotes "Boosting Class Association Rules". BCAR provides a general tool for classification tasks with various types of input data.
-
25
Data Mining Platform is a platform for data mining and analysis. It contains many of the new and sophisticated methods such as kernel-based classification, two-way clustering, bayesian networks, pattern recognition for time series analysis and many other
- Previous
- You're on page 1
- Next
