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Showing 9 open source projects for "you-get"
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Atera all-in-one platform IT management software with AI agentsAtera’s AI agents don’t just assist, they act. From detection to resolution, they handle incidents and requests instantly, taking your IT management from automated to autonomous.
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Label Studio
Label Studio is a multi-type data labeling and annotation tool
...Detect objects on image, bboxes, polygons, circular, and keypoints supported. Partition image into multiple segments. Use ML models to pre-label and optimize the process. Label Studio is an open-source data labeling tool. It lets you label data types like audio, text, images, videos, and time series with a simple and straightforward UI and export to various model formats. It can be used to prepare raw data or improve existing training data to get more accurate ML models. The frontend part of Label Studio app lies in the frontend/ folder and written in React JSX. ... -
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Diffgram
Training data (data labeling, annotation, workflow) for all data types
From ingesting data to exploring it, annotating it, and managing workflows. Diffgram is a single application that will improve your data labeling and bring all aspects of training data under a single roof. Diffgram is world’s first truly open source training data platform that focuses on giving its users an unlimited experience. This is aimed to reduce your data labeling bills and increase your Training Data Quality. Training Data is the art of supervising machines through data. This... -
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Toloka-Kit
Toloka-Kit is a Python library for working with Toloka API
Toloka-Kit is a Python library for working with Toloka API. The API allows you to build scalable and fully automated human-in-the-loop ML pipelines, and integrate them into your processes. The toolkit makes integration easier. You can use it with Jupyter Notebooks. Support for all common Toloka use cases: creating projects, adding pools, uploading tasks, and so on. Toloka entities are represented as Python classes. -
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Cleanlab
The standard data-centric AI package for data quality and ML
...See some of the datasets cleaned with cleanlab at labelerrors.com. This package helps you find label issues and other data issues, so you can train reliable ML models. All features of cleanlab work with any dataset and any model. Yes, any model: PyTorch, Tensorflow, Keras, JAX, HuggingFace, OpenAI, XGBoost, scikit-learn, etc. If you use a sklearn-compatible classifier, all cleanlab methods work out-of-the-box. -
Cloud tools for web scraping and data extractionAutomate web data collection with cloud tools that handle anti-bot measures, browser rendering, and data transformation out of the box. Extract content from any website, push to vector databases for RAG workflows, or pipe directly into your apps via API. Schedule runs, set up webhooks, and connect to your existing stack. Free tier available, then scale as you need to.
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DotVVM
Open source MVVM framework for Web Apps
...DotVVM needs less than 100 kB of JavaScript code. It's smaller than other ASP.NET-based frameworks. DotVVM offers a free Visual Studio extension giving you all the comfort you are used to. DotVVM comes with ready-made components you can use in your HTML files. The state and user interactions are handled in view models - C# classes. The controls render simple HTML which can be styled easily. MVVM pattern and data-binding expressions are used to access the UI components. -
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bbox-visualizer
Make drawing and labeling bounding boxes easy as cake
...This package helps users draw bounding boxes around objects, without doing the clumsy math that you'd need to do for positioning the labels. It also has a few different types of visualizations you can use for labeling objects after identifying them. There are optional functions that can draw multiple bounding boxes and/or write multiple labels on the same image, but it is advisable to use the above functions in a loop in order to have full control over your visualizations. -
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Lightly
A python library for self-supervised learning on images
...We provide PyTorch, PyTorch Lightning and PyTorch Lightning distributed examples for each of the models to kickstart your project. Lightly requires Python 3.6+ but we recommend using Python 3.7+. We recommend installing Lightly in a Linux or OSX environment. With lightly, you can use the latest self-supervised learning methods in a modular way using the full power of PyTorch. Experiment with different backbones, models, and loss functions. -
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Email to Calendar Event ETE
The python App/Skrypt automaticly add important events into calendar.
It is use AI running localy and model you can choose. Supproted two API first is as default is Llama, second if full LM Studio api. Skrypt have a tool for automatic add to scheduler or cron-not tested enought. Scrypt now not working with Microsoft outlook and Google gmail, for certifications and api polici reasons . Fuly tested on Seznam.cz* services provider, if you have difrent provier with same type of security or autentification it will be working. -
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Compose
A machine learning tool for automated prediction engineering
Compose is a machine learning tool for automated prediction engineering. It allows you to structure prediction problems and generate labels for supervised learning. An end user defines an outcome of interest by writing a labeling function, then runs a search to automatically extract training examples from historical data. Its result is then provided to Featuretools for automated feature engineering and subsequently to EvalML for automated machine learning. -
Grafana: The open and composable observability platformGrafana is the open source analytics & monitoring solution for every database.
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