Search Results for "hard"
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Showing 134 open source projects for "hard"
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1
Video-subtitle-extractor
A GUI tool for extracting hard-coded subtitle (hardsub) from videos
Video hard subtitle extraction, generate srt file. There is no need to apply for a third-party API, and text recognition can be implemented locally. A deep learning-based video subtitle extraction framework, including subtitle region detection and subtitle content extraction. A GUI tool for extracting hard-coded subtitles (hardsub) from videos and generating srt files. -
2
Back In Time
An easy-to-use backup tool for GNU Linux using rsync in the back
...It uses rsync to take manual or scheduled snapshots and stores them locally or remotely through SSH. Each snapshot is in its own folder with copies of the original files, but unchanged files are hard-linked between snapshots to save storage space. It was inspired by FlyBack. -
3
VideoCaptioner
AI-powered tool for generating, optimizing, and translating subtitles
...It can also translate subtitles into other languages while preserving the original timing, making it suitable for multilingual video publishing and accessibility. In addition to generating subtitles, it supports editing, formatting, and embedding subtitles into videos as either hard or soft subtitles. -
4
Starlette
The little ASGI framework that shines
...Startup and shutdown events. Test client built on httpx. CORS, GZip, Static Files, streaming responses. Session and Cookie support. 100% test coverage. 100% type annotated codebase. Few hard dependencies. Compatible with asyncio and trio backends. -
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5
Mathematics Dataset
This dataset code generates mathematical question and answer pairs
...The dataset enables models to learn mathematical problem-solving through examples that involve both numeric and symbolic reasoning. Version 1.0 includes over 2 million examples per category, with training splits labeled as “easy,” “medium,” and “hard,” supporting curriculum-based learning strategies. The data can be accessed via PyPI or generated locally using provided Python scripts, with outputs formatted for direct use in training or evaluation pipelines. -
6
supervision
We write your reusable computer vision tools
We write your reusable computer vision tools. Whether you need to load your dataset from your hard drive, draw detections on an image or video, or count how many detections are in a zone. You can count on us. -
7
srsly
Modern high-performance serialization utilities for Python
...This allows us to provide all the serialization utilities we need in a single binary wheel. Currently supports JSON, JSONL, MessagePack, Pickle, and YAML. Serialization is hard, especially across Python versions and multiple platforms. After dealing with many subtle bugs over the years (encodings, locales, large files) our libraries like spaCy and Prodigy have steadily grown a number of utility functions to wrap the multiple serialization formats we need to support (especially json, msgpack and pickle). ... -
8
Matplotlib
matplotlib: plotting with Python
Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python. Matplotlib makes easy things easy and hard things possible. Matplotlib ships with several add-on toolkits, including 3D plotting with mplot3d, axes helpers in axes_grid1 and axis helpers in axisartist. A large number of third party packages extend and build on Matplotlib functionality, including several higher-level plotting interfaces (seaborn, HoloViews, ggplot, ...), and a projection and mapping toolkit (Cartopy). ... -
9
Schemathesis
Guarantee flawless API functionality with test scenarios
...The application under test could be written in any language; the only thing you need is a valid API schema in a supported format. Simple to use and yet powerful to uncover hard-to-find errors thanks to the property-based testing approach backed by state-of-the-art Hypothesis library. Schemathesis uses your API's schema to generate both valid and invalid test scenarios, helping you verify API compliance and catch potential issues. It also verifies examples from the schema itself. -
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10
athenaSQL
SQL builder for AWS Athena, inspired by sparkSQL
athenaSQL is Athena SQL query builder, inspired by sparkSQL. It borrow some sparkSQL’s concept sparkSQL. It was initially designed to eliminate the need for hard-coding SQL queries as strings within Python scripts and as an alternative to any bespoke SQL query templating. However, it offers the flexibility to be used in various ways as needed. -
11
OpenHands
Open-source autonomous AI software engineer
Welcome to OpenHands (formerly OpenDevin), an open-source autonomous AI software engineer who is capable of executing complex engineering tasks and collaborating actively with users on software development projects. Use AI to tackle the toil in your backlog, so you can focus on what matters: hard problems, creative challenges, and over-engineering your dotfiles We believe agentic technology is too important to be controlled by a few corporations. So we're building all our agents in the open on GitHub, under the MIT license. Our agents can do anything a human developer can: they write code, run commands, and use the web. ... -
12
LangChain
⚡ Building applications with LLMs through composability ⚡
Large language models (LLMs) are emerging as a transformative technology, enabling developers to build applications that they previously could not. But using these LLMs in isolation is often not enough to create a truly powerful app - the real power comes when you can combine them with other sources of computation or knowledge. This library is aimed at assisting in the development of those types of applications. -
13
Shynet
Modern, privacy-friendly, and detailed web analytics
...They require handing all of your visitors' info to a third-party company They use cookies to track visitors across sessions, so you need to have those annoying cookie notices. They collect so much personal data that even the NSA is jealous. They are closed source and/or expensive, often with limited data portability. They are hard to use. Shynet has none of these caveats. You host it yourself, so the data is yours. It works without cookies, so you don't need any intrusive cookie notices. It collects just enough data to be useful, but not enough to be creepy. It's open source and intended to be self-hosted. And you may even find the interface easy to use. -
14
CleanVision
Automatically find issues in image datasets
...CleanVision is super simple -- run the same couple lines of Python code to audit any image dataset! The quality of machine learning models hinges on the quality of the data used to train them, but it is hard to manually identify all of the low-quality data in a big dataset. CleanVision helps you automatically identify common types of data issues lurking in image datasets. This package currently detects issues in the raw images themselves, making it a useful tool for any computer vision task such as: classification, segmentation, object detection, pose estimation, keypoint detection, generative modeling, etc. -
15
Kinto
A generic JSON document store with sharing and synchronisation options
Kinto is a minimalist JSON storage service with synchronization and sharing abilities. It is meant to be easy to use and easy to self-host. Kinto is used at Mozilla and released under the Apache v2 license. It’s hard for frontend developers to respect users' privacy when building applications that work offline, store data remotely and synchronize across devices. Existing solutions either rely on big corporations that crave user data or require a non-trivial amount of time and expertise to set up a new server for every new project. We want to help developers focus on the front, and we don’t want the challenge of storing user data to get in their way. ... -
16
kb
A minimalist command line knowledge base manager
kb is a minimalist command-line knowledge base manager that gives users a fast, organized way to collect, store, search, and retrieve notes, documents, cheatsheets, procedures, and other artifacts directly from the terminal. It was created to solve the common problem of having scattered text files or reference materials on disk that are hard to search or categorize, and it surfaces a simple CLI interface with intuitive commands for adding, viewing, editing, and deleting knowledge items. Each entry in kb can be tagged, categorized, given metadata like author or status, and inspected with full-text search or regex-based grepping, helping users quickly find content even across large knowledge collections. ... -
17
NeuralForecast
Scalable and user friendly neural forecasting algorithms.
...There is a shared belief in Neural forecasting methods' capacity to improve forecasting pipeline's accuracy and efficiency. Unfortunately, available implementations and published research are yet to realize neural networks' potential. They are hard to use and continuously fail to improve over statistical methods while being computationally prohibitive. For this reason, we created NeuralForecast, a library favoring proven accurate and efficient models focusing on their usability. -
18
tsai
Time series Timeseries Deep Learning Machine Learning Pytorch fastai
tsai is an open-source deep learning package built on top of Pytorch & fastai focused on state-of-the-art techniques for time series tasks like classification, regression, forecasting, and imputation. Starting with tsai 0.3.0 tsai will only install hard dependencies. Other soft dependencies (which are only required for selected tasks) will not be installed by default (this is the recommended approach. If you require any of the dependencies that is not installed, tsai will ask you to install it when necessary) We've also added a new PredictionDynamics callback that will display the predictions during training. ... -
19
PML
The easiest way to use deep metric learning in your application
...Anchor-positive pairs are formed by embeddings that share the same label, and anchor-negative pairs are formed by embeddings that have different labels. Loss functions can be customized using distances, reducers, and regularizers. In the diagram below, a miner finds the indices of hard pairs within a batch. These are used to index into the distance matrix, computed by the distance object. For this diagram, the loss function is pair-based, so it computes a loss per pair. -
20
EdgeDB
A next-generation graph-relational database
...Tune in for a series of lightning talks breaking down what's new and what's next for EdgeDB. EdgeDB is an open-source database designed as a spiritual successor to SQL and the relational paradigm. It aims to solve some hard design problems that make existing databases unnecessarily onerous to use. Powered by the Postgres query engine under the hood, EdgeDB thinks about schema the same way you do: as objects with properties connected by links. It's like a relational database with an object-oriented data model, or a graph database with strict schema. We call it a graph-relational database. ... -
21
Django Wiki
A wiki system with complex functionality for simple integration
...While Markdown's syntax has been influenced by several existing text-to-HTML filters -- including Setext, atx, Textile, reStructuredText, Grutatext, and EtText -- the single biggest source of inspiration for Markdown's syntax is the format of plain text email. In order to customize the wiki, best idea is to override templates and create your own template tags. Do not make your own hard copy of this repository in order to fiddle with internal parts of the wiki -- this strategy will lead you to lose out on future updates with highly improved features and plugins. Possibly security updates as well! -
22
DeepXDE
A library for scientific machine learning & physics-informed learning
...Solving forward/inverse integro-differential equations (IDEs) [SIAM Rev.] fPINN: solving forward/inverse fractional PDEs (fPDEs) [SIAM J. Sci. Comput.] NN-arbitrary polynomial chaos (NN-aPC): solving forward/inverse stochastic PDEs (sPDEs) [J. Comput. Phys.] PINN with hard constraints (hPINN): solving inverse design/topology optimization [SIAM J. Sci. Comput.] Residual-based adaptive sampling [SIAM Rev., arXiv] Gradient-enhanced PINN (gPINN) [Comput. Methods Appl. Mech. Eng.] PINN with multi-scale Fourier features [Comput. Methods Appl. Mech. Eng.] -
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lightning AI
The most intuitive, flexible, way for researchers to build models
Build in days not months with the most intuitive, flexible framework for building models and Lightning Apps (ie: ML workflow templates) which "glue" together your favorite ML lifecycle tools. Build models and build/publish end-to-end ML workflows that "glue" your favorite tools together. Models are “easy”, the “glue” work is hard. Lightning Apps are community-built templates that stitch together your favorite ML lifecycle tools into cohesive ML workflows that can run on your laptop or any cluster. Find templates (Lightning Apps), modify them and publish your own. Lightning Apps can even be full standalone ML products! Run on your laptop for free! Download the code and type 'lightning run app'. ... -
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Rhino
On-device Speech-to-Intent engine powered by deep learning
...Measure adoption, learn, and iterate. Continuously re-design and re-train to optimize engagement. Building accurate, responsive, and private voice technology is difficult. We learned the hard way, so you don’t have to. Picovoice heavily invests in R&D to offer superior voice AI that surpasses even Big Tech in accuracy and efficiency. Picovoice researchers do not follow recent frameworks and techniques but build them. -
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data-diff
Efficiently diff rows across two different databases
...Until now, there has not been any tooling to ensure that when the data is correctly copied. Replicating data at scale, across hundreds of tables, with low latency and at a reasonable infrastructure cost is a hard problem, and most data teams we’ve talked to, have faced data quality issues in their replication processes. The hard truth is that the quality of the replication is the quality of the data. Since copying entire datasets in batch is often infeasible at the modern data scale, businesses rely on the Change Data Capture (CDC) approach of replicating data using a continuous stream of updates.
