Search Results for "sandbox:/mnt/data/project_plan.pod" - Page 26
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Showing 884 open source projects for "sandbox:/mnt/data/project_plan.pod"
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Peer to Peer Recognition Brings Teams TogetherCreate a positive and energetic workplace environment with Motivosity, an innovative employee recognition and engagement platform. With Motivosity, employees can give each other small monetary bonuses for doing great things, promoting trust, collaboration, and appreciation in the workplace. The software solution comes with features such as an open-currency open-reward system, insights and analytics, dynamic organization chart, award programs, milestones, and more.
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The Industry Leading Platform for eCommerce Enablement and AnalyticsMikMak’s Where to Buy Shoppable Solutions help multichannel brands drive sales, grow market share, and increase profitability while reducing costs across categories such as CPG, Grocery, Alcohol, Beauty, Personal Care, Pet Care, Home Care, Consumer Electronics, Home Appliances, Toys, and more.
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Detic
Code release for "Detecting Twenty-thousand Classes
Detic (“Detecting Twenty-thousand Classes using Image-level Supervision”) is a large-vocabulary object detector that scales beyond fully annotated datasets by leveraging image-level labels. It decouples localization from classification, training a strong box localizer on standard detection data while learning classifiers from weak supervision and large image-tag corpora. A shared region proposal backbone feeds a flexible classification head that can expand to tens of thousands of categories without exhaustive box annotations. The system supports zero- or few-shot extension to novel categories via semantic embeddings and class name supervision, making “open-world” detection practical. ... -
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QuantResearch
Quantitative analysis, strategies and backtests
...The repository also explores financial modeling topics such as vector autoregression, Gaussian mixture models, and option pricing techniques. Many notebooks demonstrate backtesting pipelines that allow users to evaluate trading strategies using historical market data. The project integrates machine learning methods with traditional quantitative finance models, illustrating how statistical techniques can be applied to asset management and trading. -
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funNLP
Resources, corpora, and tools for Chinese natural language processing
...The project is highly community-oriented, frequently updated with contributions and new resources, and it’s widely used in both academic and applied NLP research. Its value lies in providing not just tools but also curated, domain-specific data, which can be hard to find elsewhere. -
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find-similar
User-friendly library to find similar objects
The mission of the FindSimilar project is to provide a powerful and versatile open source library that empowers developers to efficiently find similar objects and perform comparisons across a variety of data types. Whether dealing with texts, images, audio, or more, our project aims to simplify the process of identifying similarities and enhancing decision-making. https://github.com/findsimilar/find-similar - GitHub repo http://demo.findsimilar.org/ - Demo project and tutorial https://docs.findsimilar.org/ - Documentation -
MATRIX is a world-class, award-winning learning management system (LMS) for businesses.The platform is known for delivering a great user experience, while incorporating all the essential tools companies need to support efficient training and learning.
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Consistency Models
Official repo for consistency models
consistency_models is the repository for Consistency Models, a new family of generative models introduced by OpenAI that aim to generate high-quality samples by mapping noise directly into data — circumventing the need for lengthy diffusion chains. It builds on and extends diffusion model frameworks (e.g. based on the guided-diffusion codebase), adding techniques like consistency distillation and consistency training to enable fast, often one-step, sample generation. The repo is implemented in PyTorch and includes support for large-scale experiments on datasets like ImageNet-64 and LSUN variants. ... -
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LangChain Apps on Production with Jina
Langchain Apps on Production with Jina & FastAPI
...You can benefit from the scalability and serverless architecture of the cloud without sacrificing the ease and convenience of local development. And if you prefer, you can also deploy your LangChain apps on your own infrastructure to ensure data privacy. With long chain-serve, you can craft REST/WebSocket APIs, spin up LLM-powered conversational Slack bots, or wrap your LangChain apps into FastAPI packages on the cloud or on-premises. -
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macara
A converter for seamless transformation of files, data, and media ...
This application consolidates various scripts, including an AI feature (rembg), into a singular platform. The design of this software is evolutionary, allowing for the seamless integration of additional scripts, menus, or windows as needed. Serving as a versatile tool, it facilitates efficient file management, especially when handling a substantial volume of images, whether sorting by name or other attributes. These scripts are crafted to complement generative art AI technologies like Dall-e... -
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Chinese-LLaMA-Alpaca-2 v2.0
Chinese LLaMA & Alpaca large language model + local CPU/GPU training
This project has open-sourced the Chinese LLaMA model and the Alpaca large model with instruction fine-tuning to further promote the open research of large models in the Chinese NLP community. Based on the original LLaMA , these models expand the Chinese vocabulary and use Chinese data for secondary pre-training, which further improves the basic semantic understanding of Chinese. At the same time, the Chinese Alpaca model further uses Chinese instruction data for fine-tuning, which significantly improves the model's ability to understand and execute instructions. -
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MetaTransformer
Meta-Transformer for Unified Multimodal Learning
We're thrilled to present OneLLM, an ensembling Meta-Transformer framework with Multimodal Large Language Models, which performs multimodal joint training, supports more modalities including fMRI, Depth, and Normal Maps, and demonstrates very impressive performances on 25 benchmarks. -
Transforming NetOps Through No-Code Network Automation - NetBrainNetBrain, founded in 2004, provides a powerful no-code automation platform for hybrid network observability, allowing organizations to enhance their operational efficiency through automated workflows. The platform applies automation across three key workflows: troubleshooting, change management, and assessment.
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ThoughtSource
A central, open resource for data and tools
ThoughtSource is a central, open resource and community centered on data and tools for chain-of-thought reasoning in large language models (Wei 2022). Our long-term goal is to enable trustworthy and robust reasoning in advanced AI systems for driving scientific research and medical practice. -
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hloc
Visual localization made easy with hloc
...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 SfM. Just download the datasets and you're reading to go! The notebook pipeline_InLoc.ipynb shows the steps for localizing with InLoc. It's much simpler since a 3D SfM model is not needed. We show in pipeline_SfM.ipynb how to run 3D reconstruction for an unordered set of images. This generates reference poses, and a nice sparse 3D model suitable for localization with the same pipeline as Aachen. -
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AI Explainability 360
Interpretability and explainability of data and machine learning model
...The AI Explainability 360 interactive experience provides a gentle introduction to the concepts and capabilities by walking through an example use case for different consumer personas. The tutorials and example notebooks offer a deeper, data scientist-oriented introduction. The complete API is also available. There is no single approach to explainability that works best. There are many ways to explain: data vs. model, directly interpretable vs. post hoc explanation, local vs. global, etc. It may therefore be confusing to figure out which algorithms are most appropriate for a given use case. -
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AI-powered enterprise search engine
AI-powered enterprise search engine
...By leveraging natural language processing, Gerev allows users to query information in plain English, making it easier to find answers without needing exact keywords or knowing where the data is stored. The platform indexes content from connected systems rather than relying on their native search capabilities, resulting in faster and more relevant results across large datasets. Gerev is built with a strong emphasis on privacy and control, as it can be fully self-hosted, ensuring that sensitive company data remains. -
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Lightning Flash
Flash enables you to easily configure and run complex AI recipes
Your PyTorch AI Factory, Flash enables you to easily configure and run complex AI recipes for over 15 tasks across 7 data domains. In a nutshell, Flash is the production-grade research framework you always dreamed of but didn't have time to build. All data loading in Flash is performed via a from_* classmethod on a DataModule. Which DataModule to use and which from_* methods are available depends on the task you want to perform. For example, for image segmentation where your data is stored in folders, you would use the from_folders method of the SemanticSegmentationData class. ... -
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fastMRI
A large open dataset + tools to speed up MRI scans using ML
...By enabling reconstruction of high-fidelity MR images from significantly fewer measurements, fastMRI aims to make MRI scanning faster, cheaper, and more accessible in clinical settings. The repository provides an open-source PyTorch framework with data loaders, subsampling utilities, reconstruction models, and evaluation metrics, supporting both research reproducibility and practical experimentation. It includes reference implementations for key MRI reconstruction architectures such as U-Net and Variational Networks (VarNet), along with example scripts for model training and evaluation using the PyTorch Lightning framework. ... -
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MMOCR
OpenMMLab Text Detection, Recognition and Understanding Toolbox
...The toolbox supports a wide variety of state-of-the-art models for text detection, text recognition and key information extraction. The modular design of MMOCR enables users to define their own optimizers, data preprocessors, and model components such as backbones, necks and heads as well as losses. Please refer to Getting Started for how to construct a customized model. The toolbox provides a comprehensive set of utilities which can help users assess the performance of models. It includes visualizers which allow visualization of images, ground truths as well as predicted bounding boxes, and a validation tool for evaluating checkpoints. -
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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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PromethAI
Open-source framework that gives you AI Agents
PromethAI-Backend is a backend framework for AI-driven automation and knowledge extraction. It is designed to integrate with large language models (LLMs) to provide AI-enhanced workflows, including content generation, summarization, and data analysis. -
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learn2learn
A PyTorch Library for Meta-learning Research
...It provides reusable components and meta-learning algorithms, making it easier to build, train, and evaluate models that can quickly adapt to new tasks with minimal data. Learn2Learn is widely used in research for tasks such as few-shot classification, reinforcement learning, and optimization. -
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Prime QA
State-of-the-art Multilingual Question Answering research
...By using PrimeQA, a researcher can replicate the experiments outlined in a paper published in the latest NLP conference while also enjoying the capability to download pre-trained models (from an online repository) and run them on their own custom data. PrimeQA is built on top of the Transformers toolkit and uses datasets and models that are directly downloadable. -
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It is a powerful data scraping tool that helps you extract information from various online sources. Easily collect data from Google SERP, Maps, Shopify, Zillow, and more. With a user-friendly interface, you can scrape and save data in JSON or Excel formats. Unlock insights from the web effortlessly with scrape-it.cloud API.
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Metaseq
Repo for external large-scale work
...The framework was used internally at Meta to train models like OPT (Open Pre-trained Transformer) and serves as a reference implementation for scaling transformer architectures efficiently across GPUs and nodes. It supports both pretraining and fine-tuning workflows with data pipelines for text, multilingual corpora, and custom tokenization schemes. Metaseq also includes APIs for evaluation, generation, and model serving, enabling seamless transitions from training to inference. -
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Horovod
Distributed training framework for TensorFlow, Keras, PyTorch, etc.
...Horovod can be installed on-premise or run out-of-the-box in cloud platforms, including AWS, Azure, and Databricks. Horovod can additionally run on top of Apache Spark, making it possible to unify data processing and model training into a single pipeline. Once Horovod has been configured, the same infrastructure can be used to train models with any framework, making it easy to switch between TensorFlow, PyTorch, MXNet, and future frameworks as machine learning tech stacks continue to evolve. Start scaling your model training with just a few lines of Python code. ... -
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lora-svc
Singing voice change based on whisper, lora for singing voice clone
singing voice change based on whisper, and lora for singing voice clone. You will feel the beauty of the code from this project. Uni-SVC main branch is for singing voice clone based on whisper with speaker encoder and speaker adapter. Uni-SVC main target is to develop lora for SVC. With lora, maybe clone a singer just need 10 stence after 10 minutes train. Each singer is a plug-in of the base model. -
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TensorFlow Ranking
Learning to rank in TensorFlow
...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 will provide a convenient open platform for hosting and advancing state-of-the-art ranking models based on deep learning techniques, and thus facilitate both academic research and industrial applications. We provide a demo, with no installation required, to get started on using TF-Ranking. This demo runs on a colaboratory notebook, an interactive Python environment. ...
