Search Results for "state machine editor" - Page 4
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Showing 141 open source projects for "state machine editor"
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SSD in PyTorch 1.0
High quality, fast, modular reference implementation of SSD in PyTorch
This repository implements SSD (Single Shot MultiBox Detector). The implementation is heavily influenced by the projects ssd.pytorch, pytorch-ssd and maskrcnn-benchmark. This repository aims to be the code base for research based on SSD. Multi-GPU training and inference: We use DistributedDataParallel, you can train or test with arbitrary GPU(s), the training schema will change accordingly. Add your own modules without pain. We abstract backbone, Detector, BoxHead, BoxPredictor, etc. You can... -
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SMC - The State Machine Compiler
Translates state machine into a target programming language.
SMC takes a state machine stored in a .sm file and generates a State pattern in 14 programming languages. Includes: default transitions, transition args, transition guards, push/pop transitions and Entry/Exit actions. See User Manual for more info. -
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CausalNex
A Python library that helps data scientists to infer causation
CausalNex is a Python library that uses Bayesian Networks to combine machine learning and domain expertise for causal reasoning. You can use CausalNex to uncover structural relationships in your data, learn complex distributions, and observe the effect of potential interventions. -
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hloc
Visual localization made easy with hloc
This is hloc, a modular toolbox for state-of-the-art 6-DoF visual localization. It implements Hierarchical Localization, leveraging image retrieval and feature matching, and is fast, accurate, and scalable. This codebase won the indoor/outdoor localization challenges at CVPR 2020 and ECCV 2020, in combination with SuperGlue, our graph neural network for feature matching. We provide step-by-step guides to localize with Aachen, InLoc, and to generate reference poses for your own data using... -
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MMOCR
OpenMMLab Text Detection, Recognition and Understanding Toolbox
MMOCR is an open-source toolbox based on PyTorch and mmdetection for text detection, text recognition, and the corresponding downstream tasks including key information extraction. It is part of the OpenMMLab project. The toolbox supports not only text detection and text recognition, but also their downstream tasks such as key information extraction. The toolbox supports a wide variety of state-of-the-art models for text detection, text recognition and key information extraction. The modular... -
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TensorFlow Ranking
Learning to rank in TensorFlow
TensorFlow Ranking is a library for Learning-to-Rank (LTR) techniques on the TensorFlow platform. Commonly used loss functions including pointwise, pairwise, and listwise losses. Commonly used ranking metrics like Mean Reciprocal Rank (MRR) and Normalized Discounted Cumulative Gain (NDCG). Multi-item (also known as groupwise) scoring functions. LambdaLoss implementation for direct ranking metric optimization. Unbiased Learning-to-Rank from biased feedback data. We envision that this library... -
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AB3DMOT
Official Python Implementation for "3D Multi-Object Tracking
AB3DMOT is a real-time 3D multi-object tracking framework designed for applications such as autonomous driving and robotics perception. The system processes detection results from 3D object detectors that analyze LiDAR point clouds and uses them to track multiple objects across consecutive frames. Its tracking pipeline relies on a combination of classical algorithms, including a Kalman filter for state estimation and the Hungarian algorithm for data association between detected objects and... -
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OGB
Benchmark datasets, data loaders, and evaluators for graph machine
The Open Graph Benchmark (OGB) is a collection of realistic, large-scale, and diverse benchmark datasets for machine learning on graphs. OGB datasets are automatically downloaded, processed, and split using the OGB Data Loader. The model performance can be evaluated using the OGB Evaluator in a unified manner. OGB is a community-driven initiative in active development. We expect the benchmark datasets to evolve. OGB provides a diverse set of challenging and realistic benchmark datasets that... -
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Orchest
Build data pipelines, the easy way
...Each step runs a file in a container. It's that simple! Spin up services whose lifetime spans across the entire pipeline run. Easily define your dependencies to run on any machine. Run any subset of the pipeline directly or periodically. -
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Sockeye
Sequence-to-sequence framework, focused on Neural Machine Translation
Sockeye is an open-source sequence-to-sequence framework for Neural Machine Translation built on PyTorch. It implements distributed training and optimized inference for state-of-the-art models, powering Amazon Translate and other MT applications. For a quickstart guide to training a standard NMT model on any size of data, see the WMT 2014 English-German tutorial. If you are interested in collaborating or have any questions, please submit a pull request or issue. -
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OptiMate
Libraries for optimizing AI models, inference speed, and GPU usage
...One of the core components, Speedster, focuses on accelerating model inference by applying state of the art optimization techniques to increase performance while lowering operational costs. Another component, Nos, targets infrastructure optimization by improving GPU utilization in Kubernetes clusters through dynamic partitioning and elastic resource quotas. -
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scanless
Online port scan scraper
scanless is a Python command-line utility and library for running port scans through third-party online scanning services. It is built for users who want quick visibility into exposed ports without running a local scanner directly from their own machine. The tool can scan an IP address or domain, choose a specific supported scanner, select a random scanner, or run all available scanners. It supports services such as ipfingerprints, spiderip, standingtech, viewdns, and yougetsignal. scanless can return raw scan output as well as parsed results that include port, state, service, and protocol details. ... -
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Classic HWUT
Software Unit Tests (Language Independent Approach)
...In particular for C, HWUT supports make file generation using 'sos' and 'sols' modes. Remote control-able function stubs may be generated using the 'stub' mode. Test cases can be generated using the 'gen' mode, and state machine walkers by the 'sm_walker' mode. Over the last decade HWUT has matured towards a full fledged unit test tool for a wide variety of applications. -
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Karate Club
An API Oriented Open-source Python Framework for Unsupervised Learning
Karate Club is an unsupervised machine learning extension library for NetworkX. Karate Club consists of state-of-the-art methods to do unsupervised learning on graph-structured data. To put it simply it is a Swiss Army knife for small-scale graph mining research. First, it provides network embedding techniques at the node and graph level. Second, it includes a variety of overlapping and non-overlapping community detection methods. -
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BEVFormer
Implementation of BEVFormer, a camera-only framework
3D visual perception tasks, including 3D detection and map segmentation based on multi-camera images, are essential for autonomous driving systems. In this work, we present a new framework termed BEVFormer, which learns unified BEV representations with spatiotemporal transformers to support multiple autonomous driving perception tasks. In a nutshell, BEVFormer exploits both spatial and temporal information by interacting with spatial and temporal space through predefined grid-shaped BEV... -
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Fairseq
Facebook AI Research Sequence-to-Sequence Toolkit written in Python
Fairseq(-py) is a sequence modeling toolkit that allows researchers and developers to train custom models for translation, summarization, language modeling and other text generation tasks. We provide reference implementations of various sequence modeling papers. Recent work by Microsoft and Google has shown that data parallel training can be made significantly more efficient by sharding the model parameters and optimizer state across data parallel workers. These ideas are encapsulated in the... -
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Guild AI
Experiment tracking, ML developer tools
Guild AI is an open-source experiment tracking toolkit designed to bring systematic control to machine learning workflows, enabling users to build better models faster. It automatically captures every detail of training runs as unique experiments, facilitating comprehensive tracking and analysis. Users can compare and analyze runs to deepen their understanding and incrementally improve models. Guild AI simplifies hyperparameter tuning by applying state-of-the-art algorithms through straightforward commands, eliminating the need for complex trial setups. ... -
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Deep learning time series forecasting
Deep learning PyTorch library for time series forecasting
Example image Flow Forecast (FF) is an open-source deep learning for time series forecasting framework. It provides all the latest state-of-the-art models (transformers, attention models, GRUs) and cutting-edge concepts with easy-to-understand interpretability metrics, cloud provider integration, and model serving capabilities. Flow Forecast was the first time series framework to feature support for transformer-based models and remains the only true end-to-end deep learning for time series... -
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LayoutParser
A Unified Toolkit for Deep Learning Based Document Image Analysis
With the help of state-of-the-art deep learning models, Layout Parser enables extracting complicated document structures using only several lines of code. This method is also more robust and generalizable as no sophisticated rules are involved in this process. A complete instruction for installing the main Layout Parser library and auxiliary components. Learn how to load DL Layout models and use them for layout detection. The full list of layout models currently available in Layout Parser.... -
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YOLOv3
Object detection architectures and models pretrained on the COCO data
Fast, precise and easy to train, YOLOv5 has a long and successful history of real time object detection. Treat YOLOv5 as a university where you'll feed your model information for it to learn from and grow into one integrated tool. You can get started with less than 6 lines of code. with YOLOv5 and its Pytorch implementation. Have a go using our API by uploading your own image and watch as YOLOv5 identifies objects using our pretrained models. Start training your model without being an... -
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Interpret-Text
State-of-the-art explainers for text-based machine learning models
...Users can run their experiments across multiple state-of-the-art explainers and easily perform comparative analysis on them. Using these tools, users will be able to explain their machine-learning models globally on each label or locally for each document. -
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igel
Machine learning tool that allows you to train and test models
A delightful machine learning tool that allows you to train/fit, test, and use models without writing code. The goal of the project is to provide machine learning for everyone, both technical and non-technical users. I sometimes needed a tool sometimes, which I could use to fast create a machine learning prototype. Whether to build some proof of concept, create a fast draft model to prove a point or use auto ML. I find myself often stuck writing boilerplate code and thinking too much about... -
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MuJoCo-py
mujoco-py allows using MuJoCo from Python 3
mujoco-py is a Python wrapper for MuJoCo, a high-performance physics engine widely used in robotics, reinforcement learning, and AI research. It allows developers and researchers to run detailed rigid body simulations with contacts directly from Python, making MuJoCo easier to integrate into machine learning workflows. The library is compatible with MuJoCo version 2.1 and supports Linux and macOS, while Windows support has been deprecated. It provides utilities for loading models, running simulations, and accessing simulation states in real time, along with visualization tools for rendering environments. The project also includes interactive examples showcasing collision handling, texture randomization, state resetting, and robot control. ... -
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Argos Translate
Open-source offline translation library written in Python
Argos Translate uses OpenNMT for translations and can be used as either a Python library, command-line, or GUI application. Argos Translate supports installing language model packages which are zip archives with a ".argosmodel" extension containing the data needed for translation. LibreTranslate is an API and web-app built on top of Argos Translate. Argos Translate also manages automatically pivoting through intermediate languages to translate between languages that don't have a direct... -
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SparrowRecSys
A Deep Learning Recommender System
SparrowRecSys is an open-source deep learning recommendation system framework designed to demonstrate the architecture and implementation of modern industrial-scale recommender systems. The project integrates multiple machine learning models and data processing pipelines to simulate how real-world recommendation platforms operate. It includes components for offline data processing, feature engineering, model training, real-time data updates, and online recommendation services. SparrowRecSys supports a wide range of state-of-the-art recommendation algorithms, including models for click-through rate prediction and user behavior modeling that are widely used in advertising and content recommendation systems. ...