Search Results for "training" - Page 2
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Showing 52 open source projects for "training"
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Stop Storing Third-Party Tokens in Your DatabaseRolling your own OAuth token storage can be a security liability. Token Vault securely stores access and refresh tokens from federated providers and handles exchange and renewal automatically. Connected accounts, refresh exchange, and privileged worker flows included.
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Go From AI Idea to AI App FastAccess Gemini 3 and 200+ models. Build chatbots, agents, or custom models with built-in monitoring and scaling.
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KotlinDL
High-level Deep Learning Framework written in Kotlin
KotlinDL is a high-level Deep Learning API written in Kotlin and inspired by Keras. Under the hood, it uses TensorFlow Java API and ONNX Runtime API for Java. KotlinDL offers simple APIs for training deep learning models from scratch, importing existing Keras and ONNX models for inference, and leveraging transfer learning for tailoring existing pre-trained models to your tasks. This project aims to make Deep Learning easier for JVM and Android developers and simplify deploying deep learning models in production environments. -
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ClassyVision
An end-to-end PyTorch framework for image and video classification
...It offers high performance and scalability—capable of training models like ResNet-50 on ImageNet in just minutes—while remaining accessible to both researchers and production engineers. The library integrates seamlessly with PyTorch Hub for easy access to pretrained models and supports elastic training using PyTorch Elastic, making distributed training robust to changes in cluster resources or hardware failures. -
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UnionML
Build and deploy machine learning microservices
...Fit the rich ecosystem of tools and frameworks into a common protocol for machine learning. Using industry-standard machine learning methods, implement endpoints for fetching data, training models, serving predictions (and much more) to write a complete ML stack in one place. Data science, ML engineering, and MLOps practitioners can all gather around UnionML apps as a way of defining a single source of truth about your ML system’s behavior. This helps you maintain consistent code across your ML stack, from training to prediction logic. -
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Darknet
Convolutional Neural Networks
...Darknet is lightweight, fast, and easy to compile, making it suitable for research and production use. The repository provides pre-trained models, configuration files, and tools for training custom object detection models. With GPU acceleration via CUDA and OpenCV integration, it achieves high performance in image recognition tasks. Its simplicity, combined with powerful capabilities, has made Darknet one of the most influential projects in the computer vision community. -
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.
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Blankly
Easily build, backtest and deploy your algo in just a few lines
...Models can be instantly backtested, paper traded, sandbox tested and run live by simply changing a single line. We built blankly for every type of quant including training & running ML models in the same environment, cross-exchange/cross-symbol arbitrage, and even long/short positions on stocks (all with built-in WebSockets). Blankly is the first framework to enable developers to backtest, paper trade, and go live across exchanges without modifying a single line of trading logic on stocks, crypto, and forex. ... -
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Model Search
Framework that implements AutoML algorithms
...Instead of hand-crafting models, you define a search space and objectives, then the system explores candidate architectures using controllers and population-based strategies. It supports multiple tasks (such as vision or text) by letting you express reusable building blocks—layers, cells, and topologies—that the search can recombine. Training, evaluation, and promotion of candidates are orchestrated automatically, with strong emphasis on reproducibility and fair comparisons. The framework logs trials, metrics, and artifacts so you can analyze what the search learned and why certain designs dominate. It’s intended as a platform for method development as much as for model discovery. -
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TorchGAN
Research Framework for easy and efficient training of GANs
The torchgan package consists of various generative adversarial networks and utilities that have been found useful in training them. This package provides an easy-to-use API which can be used to train popular GANs as well as develop newer variants. The core idea behind this project is to facilitate easy and rapid generative adversarial model research. TorchGAN is a Pytorch-based framework for designing and developing Generative Adversarial Networks. This framework has been designed to provide building blocks for popular GANs and also to allow customization for cutting-edge research. ... -
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Gluon CV Toolkit
Gluon CV Toolkit
GluonCV provides implementations of state-of-the-art (SOTA) deep learning algorithms in computer vision. It aims to help engineers, researchers, and students quickly prototype products, validate new ideas and learn computer vision. It features training scripts that reproduce SOTA results reported in latest papers, a large set of pre-trained models, carefully designed APIs and easy-to-understand implementations and community support. From fundamental image classification, object detection, semantic segmentation and pose estimation, to instance segmentation and video action recognition. ... -
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PyTorch SimCLR
PyTorch implementation of SimCLR: A Simple Framework
...These large models are trained on huge supervised corpora, like the ImageNet. And most important, their features are known to adapt well to new problems. This is particularly interesting when annotated training data is scarce. In situations like this, we take the models’ pre-trained weights, append a new classifier layer on top of it, and retrain the network. This is called transfer learning, and is one of the most used techniques in CV. Aside from a few tricks when performing fine-tuning (if the case), it has been shown (many times) that if training for a new task, models initialized with pre-trained weights tend to learn faster and be more accurate then training from scratch using random initialization. -
Add Two Lines of Code. Get Full APM.Works out of the box for Rails, Django, Express, Phoenix, and more. Monitoring exceptions and performance in no time.
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PyText
A natural language modeling framework based on PyTorch
...It achieves this by providing simple and extensible interfaces and abstractions for model components, and by using PyTorch’s capabilities of exporting models for inference via the optimized Caffe2 execution engine. We use PyText at Facebook to iterate quickly on new modeling ideas and then seamlessly ship them at scale. Distributed-training support built on the new C10d backend in PyTorch 1.0. Mixed precision training support through APEX (trains faster with less GPU memory on NVIDIA Tensor Cores). Extensible components that allows easy creation of new models and tasks. -
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TensorFlow Object Counting API
The TensorFlow Object Counting API is an open source framework
...Please contact if you need professional object detection & tracking & counting project with super high accuracy and reliability! You can train TensorFlow models with your own training data to built your own custom object counter system! If you want to learn how to do it, please check one of the sample projects, which cover some of the theory of transfer learning and show how to apply it in useful projects. The development is on progress! The API will be updated soon, the more talented and light-weight API will be available in this repo! ... -
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CrypTen
A framework for Privacy Preserving Machine Learning
...The framework supports both encryption and decryption of tensors and operations such as addition and multiplication over encrypted values. Although not yet production-ready, CrypTen focuses on advancing real-world secure ML applications, such as training and inference over private datasets, without exposing sensitive data. -
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Ness PHP
A php framework with zero configuration.
Website: https://nessphp.github.io GitHub: https://github.com/nessphp Documentation: https://nessphp.github.io/docs/index.html Donate: https://paypal.me/sinansalichasan Welcome to Ness PHP Framework. Do you need a web framework with minimized training effort? Ness PHP offers you a model-view-controller based environment for coding faster, safer and stronger web applications with (nearly) zero configuration. Get rid of mess and focus the main logic of your project Ness PHP is an excellent but straightforward application development framework which aims to help you in your coding process by speeding up development with ready to use libraries that require minimal configuration. -
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MMF
A modular framework for vision & language multimodal research
...MMF contains reference implementations of state-of-the-art vision and language models and has powered multiple research projects at Facebook AI Research. MMF is designed from ground up to let you focus on what matters, your model, by providing boilerplate code for distributed training, common datasets and state-of-the-art pre-trained baselines out-of-the-box. MMF is built on top of PyTorch that brings all of its power in your hands. MMF is not strongly opinionated. So you can use all of your PyTorch knowledge here. MMF is created to be easily extensible and composable. Through our modular design, you can use specific components from MMF that you care about. ... -
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X-DeepLearning
An industrial deep learning framework for high-dimension sparse data
...Storage and communication optimization, parameters are automatically allocated globally without manual intervention, and requests are merged to completely eliminate computing/storage/communication hotspots of ps. Complete streaming training features including feature admission, feature elimination, model incremental export, feature counting statistics, etc. Background: XDL1.0 focuses on throughput optimization and adopts the one request per thread processing model, which can significantly improve the limit throughput under ultra-high concurrency. -
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RefineNet
RefineNet: Multi-Path Refinement Networks
...It provides trained models for datasets such as PASCAL VOC 2012, Cityscapes, NYUDv2, Person_Parts, PASCAL_Context, SUNRGBD, and ADE20k, with versions based on ResNet-101 and ResNet-152 backbones. The repository supports both single-scale and multi-scale prediction, with scripts for training, testing, and evaluating segmentation performance. While this codebase is specific to MATLAB and MatConvNet, a PyTorch implementation and lighter-weight variants are also available from the community. -
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Gin Config
Gin provides a lightweight configuration framework for Python
...Gin is particularly popular in TensorFlow and PyTorch projects, where researchers and developers need to tune numerous interdependent parameters across models, datasets, optimizers, and training pipelines. -
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ChunJun
A data integration framework
ChunJun is a distributed integration framework, and currently is based on Apache Flink. It was initially known as FlinkX and renamed ChunJun on February 22, 2022. It can realize data synchronization and calculation between various heterogeneous data sources. ChunJun has been deployed and running stably in thousands of companies so far. Based on the real-time computing engine--Flink, and supports JSON template and SQL script configuration tasks. The SQL script is compatible with Flink SQL... -
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Mocha.jl
Deep Learning framework for Julia
Mocha.jl is a deep learning framework for Julia, inspired by the C++ Caffe framework. It offers efficient implementations of gradient descent solvers and common neural network layers, supports optional unsupervised pre-training, and allows switching to a GPU backend for accelerated performance. The development of Mocha.jl happens in relative early days of Julia. Now that both Julia and the ecosystem has evolved significantly, and with some exciting new tech such as writing GPU kernels directly in Julia and general auto-differentiation supports, the Mocha codebase becomes excessively old and primitive. ... -
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Accord.NET Framework
Machine learning, computer vision, statistics and computing for .NET
...It can be used on Microsoft Windows, Xamarin, Unity3D, Windows Store applications, Linux or mobile. After merging with the AForge.NET project, the framework now offers a unified API for learning/training machine learning models that is both easy to use and extensible. -
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seq2seq
A general-purpose encoder-decoder framework for Tensorflow
seq2seq is an early, influential TensorFlow reference implementation for sequence-to-sequence learning with attention, covering tasks like neural machine translation, summarization, and dialogue. It packaged encoders, decoders, attention mechanisms, and beam search into a modular training and inference framework. The codebase showcased best practices for batching, bucketing by sequence length, and handling variable-length sequences efficiently on GPUs. Researchers used it as a baseline to reproduce classic results and to prototype new attention variants and training tricks. It also offered scripts for data preprocessing, evaluation, and exporting models for serving. ... -
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TestMax
Frontend & Backend Automation Tool
TestMax is integrated Software Testing framework which can be used to test distributed system by developing rapid test cases using XML configuration in front and backend that talks to each other within the test framework. This tool can be used for Database, webservice, API, Junit, TestNg and Frontend including performance test. The main advantage of this TestMax architecture is separating test data configuration from test case run time environment. It reduces the maintenance of test cases... -
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cognity
A neural network library for Java.
Cognity is an object-oriented neural network library for Java. It's goal is to provide easy-to-use, high level architecture for neural network computations along with reasonable performance. -
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Concrete CMS
Open Source Content Management System for teams.
...You can have the best of both worlds and run a secure website your content contributors will love using with Concrete CMS. The user experience is built around in-context editing, it’s as easy to use as a word processor. You'll spend less time training people, and less time having to fix things yourself. As an open source framework you can build complex applications as features like permissions, workflow, file management, calendar, forms, SEO and so much more are built right in. A marketplace of add-ons & themes and active community can help you finish building an amazing product using Concrete CMS. -
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This is class can be used as a tool for optical character recognition. It can recognize text in monochrome graphical images after a training phase. The training phase is necessary to let the class build recognition data structures from images that have
