Search Results for "model-builder" - Page 30
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Showing 1820 open source projects for "model-builder"
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Jraph
A Graph Neural Network Library in Jax
...The library includes a comprehensive set of utilities for batching, padding, masking, and partitioning graph data, making it ideal for distributed and large-scale GNN experiments. Jraph also comes with a model zoo—a collection of forkable reference implementations of common message-passing GNN architectures, such as Graph Networks, Graph Convolutional Networks, and Graph Attention Networks. -
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Smoothie Charts
Smooth JavaScript charts for realtime streaming data
...Data points can be appended continuously, making it useful for dashboards, monitoring tools, server metrics, sensors, and other live feeds. The project includes examples, documentation, an interactive builder, and installation options through npm, Yarn, Bower, or direct JavaScript usage. Overall, it is a lightweight option for adding fluid, browser-based streaming charts. -
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PyTorch Transfer-Learning-Library
Transfer Learning Library for Domain Adaptation, Task Adaptation, etc.
TLlib is an open-source and well-documented library for Transfer Learning. It is based on pure PyTorch with high performance and friendly API. Our code is pythonic, and the design is consistent with torchvision. You can easily develop new algorithms or readily apply existing algorithms. We appreciate all contributions. If you are planning to contribute back bug-fixes, please do so without any further discussion. If you plan to contribute new features, utility functions or extensions, please... -
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YOLOV4 Pytorch
This is a source code for YoloV4-pytorch that can be used to train you
YOLOV4 Pytorch is a PyTorch implementation of the YOLOv4 object detection model for training and running custom detection systems. The repository is structured around practical workflows, including training, prediction, evaluation, anchor generation, model configuration, and dataset annotation utilities. It supports VOC-style datasets and includes scripts for prediction, mAP evaluation, FPS testing, video prediction, batch prediction, and heatmap generation. -
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Official YOLOv7
YOLOv7: Trainable bag-of-freebies sets new state-of-the-art
YOLOv7 is the official implementation of the paper “YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors.” It is a PyTorch-based object detection project focused on high speed and strong accuracy for real-time computer vision. The repository provides model definitions, training scripts, testing tools, inference examples, pretrained weights, and deployment-oriented materials. YOLOv7 introduced training-time improvements that raise accuracy without increasing inference cost, which is why the project became important in real-time detection research. It supports multiple model sizes and related tasks such as object detection and instance segmentation through associated branches or weights. ... -
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YOLOV3 Pytorch
This is a source code for yolo3-pytorch
YOLOV3 Pytorch is a PyTorch implementation of the YOLOv3 object detection model built for training, prediction, and evaluation. The repository provides a complete workflow for users who want to train their own object detector with VOC-style data or use pretrained weights. It includes utilities for annotation conversion, anchor generation, image prediction, video prediction, batch prediction, FPS measurement, heatmap output, and mAP evaluation. -
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Fairseq
Facebook AI Research Sequence-to-Sequence Toolkit written in Python
...Tasks store dictionaries and provide helpers for loading/iterating over Datasets, initializing the Model/Criterion and calculating the loss. -
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Flowy
The minimal javascript library to create flowcharts
...The library focuses on simplicity and aesthetics, offering a clean look out of the box that can be customized with your own CSS, making it suitable for dashboards, SaaS products, or internal tools. Events emitted by flowy enable integration with your own data model, so you can sync block positions, connections, and metadata back to a database or application state. It is framework-agnostic, so it can be dropped into vanilla JavaScript projects or wrapped inside React, Vue, or other frameworks. -
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asm68c
Assembles M6800 and 6801 assembly language source code. (and Forth)
A simple absolute mode assembler for 6800/6801. Handles the complete published set of opcodes/mnemonics. Mostly source-language compatible with the common assemblers in use circa 1985, but no macros. Now uses standard LDAA syntax by default. Produces basic S1/S9 object files and assembly listings, and now binary and MC-10 compatible cassette images. (This is for the 8-bit 6800, not the 16/32 bit 68000/coldfire processors.) -
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SageMaker MXNet Inference Toolkit
Toolkit for allowing inference and serving with MXNet in SageMaker
SageMaker MXNet Inference Toolkit is an open-source library for serving MXNet models on Amazon SageMaker. This library provides default pre-processing, predict and postprocessing for certain MXNet model types and utilizes the SageMaker Inference Toolkit for starting up the model server, which is responsible for handling inference requests. AWS Deep Learning Containers (DLCs) are a set of Docker images for training and serving models in TensorFlow, TensorFlow 2, PyTorch, and MXNet. Deep Learning Containers provide optimized environments with TensorFlow and MXNet, Nvidia CUDA (for GPU instances), and Intel MKL (for CPU instances) libraries and are available in the Amazon Elastic Container Registry (Amazon ECR). ... -
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Blankly
Easily build, backtest and deploy your algo in just a few lines
...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. Every model needs to figure out how to buy and sell. We make it super easy for you so you can focus on building better trading algos. Your models can run on any platform, and on any supported exchange. We make that as easy as just changing one line of code. -
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Real-ESRGAN ncnn Vulkan
NCNN implementation of Real-ESRGAN
...Unlike the standard PyTorch-based Real-ESRGAN code, this variant is written in C/C++ and designed to run efficiently on many platforms (including Windows, Linux, and possibly Android) without requiring heavy frameworks like CUDA or Python. It provides command-line tools for upscaling images with selected models, allowing users to specify input/output paths, scaling factors, tile sizes, and model names from a compressed model set, which is particularly helpful for larger images or automated workflows. The Vulkan backend enables fast execution on GPUs from different vendors (Intel/AMD/Nvidia) with broad support, making it suitable for non-Python environments, production systems, or performance-constrained setups. -
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FlatCC FlatBuffers in C for C
FlatBuffers Compiler and Library in C for C
flatcc has no external dependencies except for build and compiler tools, and the C runtime library. With concurrent Ninja builds, a small client project can build flatcc with libraries, generate schema code, link the project and execute a test case in a few seconds, produce binaries between 15K and 60K, read small buffers in 30ns, build FlatBuffers in about 600ns, and with a larger executable also handle optional json parsing or printing in less than 2 us for a 10 field mixed type message.... -
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CoolplaySpark
Spark Cool Play: Spark source code analysis, Spark class library, etc.
...It serves as a companion resource for the book 深入理解Spark核心思想与源码分析 (In-Depth Understanding of Spark’s Core Concepts and Source Code Analysis). The project contains annotated examples, explanations, and exercises that guide learners through Spark’s architecture, execution model, and source code internals. It is particularly valuable for developers who want to strengthen their understanding of Spark by not only using it as a data processing engine but also exploring how its internals function. Through code analysis and commentary, CoolplaySpark helps readers connect theoretical concepts with practical implementation details. ... -
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Model Search
Framework that implements AutoML algorithms
...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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apl-library
Useful stuff for APL programmers
This collection of APL libraries have been contributed to GNU APL and should be downloaded here: https://gnu.org/software/apl. -
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Provider
InheritedWidgets, but simple
...Because it relies on Flutter’s core mechanisms, it integrates seamlessly with common architectural styles and existing widgets. Many apps use provider as a baseline pattern, and later evolve toward more advanced solutions as complexity grows—without discarding the conceptual model provider introduces. -
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TARS
Markup builder on gulp
...To bypass dependency installation for every TARS project, TARS-CLI was created. As such, TARS is not a npm package by itself. This decision was made so that everyone could comfortably customize the builder for themselves. TARS-CLI is just a simple builder interface, which includes all dependencies for TARS. It is strongly recommended to use TARS-CLI for development. -
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Django REST Framework User
This Django User Model is customised user model
This Django User Model is a customized user model keeping in mind the practical need. JWT Support (Using Simple JWT) Mobile Number, single field for full name, REST API to register, REST API to login, MultiModelBackend: User can login using either of mobile, email or username. REST API to login with OTP (Same API endpoint as for OTP Verification; Set is_login: true while sending JSON request). -
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Faster-Rcnn
This is a pytorch implementation library of faster-rcnn
Faster-Rcnn is a PyTorch implementation of the Faster R-CNN two-stage object detection model. It is designed for training and evaluating detectors on VOC-format datasets, including VOC07+12 and custom datasets arranged with VOC-style annotations and images. The repository includes scripts for training, prediction, evaluation, annotation generation, and model summary inspection. It supports backbone options through pretrained VGG and ResNet weights, making it useful for comparing feature extractors. ... -
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graphql-client
A Ruby library for declaring, composing and executing GraphQL queries
...If you haven't already, familiarize yourself with the GraphQL query syntax. Queries are declared with the same syntax inside of a <<-'GRAPHQL' heredoc. There isn't any special query builder Ruby DSL. This client library encourages all GraphQL queries to be declared statically and assigned to a Ruby constant. If you're using Ruby on Rails ERB templates, theres a ERB extension that allows static queries to be defined in the template itself. In standard Ruby you can simply assign queries and fragments to constants and they'll be available throughout the app. ... -
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SwagGen
OpenAPI/Swagger 3.0 Parser and Swift code generator
...Templates for any language can be written that leverage this generator. SwagGen includes a bundled template for generating a client-side Swift library for interfacing with the Swagger spec. It includes support for model inheritance, shared enums, discrete and mutable request objects, inline schemas, Codable and Equatable models, configurable options, generic networking stack, and many other niceties. -
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Unet
Source code for unet-pytorch, which can train its own model
...The repository is built around training, prediction, and mIoU evaluation for VOC-style segmentation data and medical-style datasets. It includes scripts for general training, medical dataset training, prediction, annotation handling, model summaries, and evaluation. The project supports multiple backbones, data processing utilities, extensive comments, and adjustable training parameters. Its README notes that U-Net is better suited to datasets with fewer features and shallow visual structures, such as medical image segmentation, rather than complex VOC-style scenes. ... -
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Vale
Compiler for the Vale programming language
Vale is a systems programming language and compiler that strives to combine performance, safety, and ease in a way that avoids many of the complexities of traditional memory management techniques. It is ahead-of-time (AOT) compiled, targeting LLVM as a backend, and is statically typed. The language introduces a concept called generational references to provide memory safety without relying on a garbage collector, and aims for “safe without a borrow checker” (i.e. reducing the burden on the... -
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Node.js express.js MongoDB JWT REST API
Node.js express.js MongoDB JWT REST API - Basic Project Skeleton
This is a basic API REST skeleton written on JavaScript using async/await. Great for building a starter web API for your front-end (Android, iOS, Vue, react, angular, or anything that can consume an API) This project is created to help other developers create a basic REST API in an easy way with Node.js. This basic example shows how powerful and simple JavaScript can be. Do you want to contribute? Pull requests are always welcome to show more features. Custom email/password user system with...
