Search Results for "raw"
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Showing 26 open source projects for "raw"
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1
OpenBB
Investment Research for Everyone, Everywhere
...Don’t waste time reading, create summaries in seconds and ask how that impacts investments. Create your dashboard with your favorite widgets. Create charts directly from raw data in seconds. Create charts directly from raw data in seconds. Customize your dashboards to build your dream terminal, integrate with your private datasets and bring your own fine-tuned AI copilots. -
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deepfakes_faceswap
Deepfakes Software For All
Faceswap is the leading free and open source multi-platform deepfakes software. When faceswapping was first developed and published, the technology was groundbreaking, it was a huge step in AI development. It was also completely ignored outside of academia because the code was confusing and fragmentary. It required a thorough understanding of complicated AI techniques and took a lot of effort to figure it out. Until one individual brought it together into a single, cohesive collection. -
3
Label Studio
Label Studio is a multi-type data labeling and annotation 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. Multi-user labeling sign up and login, when you create an annotation it's tied to your account. Configurable label formats let you customize the visual interface to meet your specific labeling needs. ... -
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GROBID
A machine learning software for extracting information
GROBID is a machine learning library for extracting, parsing, and re-structuring raw documents such as PDF into structured XML/TEI encoded documents with a particular focus on technical and scientific publications. First developments started in 2008 as a hobby. In 2011 the tool has been made available in open source. Work on GROBID has been steady as a side project since the beginning and is expected to continue as such. -
MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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5
LibrePhotos
A self-hosted open source photo management service
LibrePhotos is an open-source self-hosted photo management platform designed to organize, browse, and analyze personal media libraries while preserving user privacy. The system allows individuals to store and manage their photos and videos locally rather than relying on commercial cloud services. It provides features similar to services like Google Photos but runs on a private server controlled by the user. The application includes AI-powered tools that automatically analyze images to detect... -
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AutoGluon
AutoGluon: AutoML for Image, Text, and Tabular Data
AutoGluon enables easy-to-use and easy-to-extend AutoML with a focus on automated stack ensembling, deep learning, and real-world applications spanning image, text, and tabular data. Intended for both ML beginners and experts, AutoGluon enables you to quickly prototype deep learning and classical ML solutions for your raw data with a few lines of code. Automatically utilize state-of-the-art techniques (where appropriate) without expert knowledge. Leverage automatic hyperparameter tuning, model selection/ensembling, architecture search, and data processing. Easily improve/tune your bespoke models and data pipelines, or customize AutoGluon for your use-case. ... -
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FISSURE
The RF and reverse engineering framework for everyone
...The project brings together tools for capturing, inspecting, decoding, replaying, and analyzing RF signals across a wide range of wireless technologies. It is designed as a practical environment for researchers and operators who need to move from raw spectrum observation to structured investigation without stitching together too many separate utilities by hand. The platform supports workflows related to signal discovery, demodulation, packet inspection, fuzzing, and attack simulation, making it useful for both defensive research and controlled lab testing. Its architecture is oriented toward extensibility, so users can integrate additional hardware, signal-processing components, and protocol-specific modules depending on their needs. -
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Lightly
A python library for self-supervised learning on images
...We, at Lightly, are passionate engineers who want to make deep learning more efficient. That's why - together with our community - we want to popularize the use of self-supervised methods to understand and curate raw image data. Our solution can be applied before any data annotation step and the learned representations can be used to visualize and analyze datasets. This allows selecting the best core set of samples for model training through advanced filtering. We provide PyTorch, PyTorch Lightning and PyTorch Lightning distributed examples for each of the models to kickstart your project. ... -
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Diffgram
Training data (data labeling, annotation, workflow) for all data types
...Training Data is the art of supervising machines through data. This includes the activities of annotation, which produces structured data; ready to be consumed by a machine learning model. Annotation is required because raw media is considered to be unstructured and not usable without it. That’s why training data is required for many modern machine learning use cases including computer vision, natural language processing and speech recognition. -
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10
Flower
Flower: A Friendly Federated Learning Framework
...Flower can be used with any machine learning framework, for example, PyTorch, TensorFlow, Hugging Face Transformers, PyTorch Lightning, scikit-learn, JAX, TFLite, MONAI, fastai, MLX, XGBoost, Pandas for federated analytics, or even raw NumPy for users who enjoy computing gradients by hand. -
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pyAudioAnalysis
Python Audio Analysis Library: Feature Extraction, Classification
...The project provides a collection of tools that allow developers to extract meaningful features from audio files and use those features for classification, segmentation, and analysis. The library supports multiple audio processing workflows, including feature extraction from raw audio signals, training of machine learning models, and automatic audio segmentation. It also includes utilities for visualizing audio features and analyzing patterns within sound recordings, which can be useful in applications such as speech recognition, music classification, and acoustic event detection. Because the library integrates machine learning algorithms with signal processing tools, it enables researchers to develop complete audio analysis pipelines using a single framework. -
12
Loss
The Brutalist Market Analyzer
...LOSS solves this by automating the math and reading the news for you. Instead of a monolithic architecture, LOSS uses a Split-Stack approach: A Vercel Relay handles raw data fetching, bypassing CORS and rate limits. A Render Python Backend acts as the "Brain," executing heavy machine learning and NLP tasks. Try the live application in action here: https://loss-by-vignesh.vercel.app before downloading the source code Visit For More Details : https://github.com/Vignesh-72/Loss -
13
MuseGAN
An AI for Music Generation
...The system focuses specifically on generating multi-track polyphonic music, meaning that it can simultaneously produce multiple instrument parts such as drums, bass, piano, guitar, and strings. Instead of generating raw audio, the model operates on piano-roll representations of music, which encode notes as time-pitch matrices for each instrument track. This representation allows the neural network to capture rhythmic patterns, harmonic relationships, and structural dependencies across instruments. The architecture is based on convolutional GAN models that learn temporal musical structure and inter-track relationships from training data. ... -
14
Featuretools
An open source python library for automated feature engineering
An open source Python framework for automated feature engineering. Featuretools automatically creates features from temporal and relational datasets. Featuretools uses DFS for automated feature engineering. You can combine your raw data with what you know about your data to build meaningful features for machine learning and predictive modeling. Featuretools provides APIs to ensure only valid data is used for calculations, keeping your feature vectors safe from common label leakage problems. You can specify prediction times row-by-row. Featuretools come with a library of low-level functions that can be stacked to create features. ... -
15
ADAMS
ADAMS is a workflow engine for building complex knowledge workflows.
ADAMS is a flexible workflow engine aimed at quickly building and maintaining data-driven, reactive workflows, easily integrated into business processes. Instead of placing operators on a canvas and manually connecting them, a tree structure and flow control operators determine how data is processed (sequentially/parallel). This allows rapid development and easy maintenance of large workflows, with hundreds or thousands of operators. Operators include machine learning (WEKA, MOA, MEKA)... -
16
fe4ml-zh
Feature Engineering for Machine Learning
...The repository aims to make advanced feature engineering concepts accessible to a broader audience by translating the content and organizing it into readable documentation and code examples. Feature engineering is a critical component of machine learning pipelines because it determines how raw data is transformed into features that algorithms can use effectively. The project explains techniques for creating, selecting, and transforming features in ways that improve model accuracy and robustness. It also discusses the role of domain knowledge, data preprocessing, and statistical reasoning in building effective machine learning models. -
17
sense2vec
Contextually-keyed word vectors
sense2vec (Trask et. al, 2015) is a nice twist on word2vec that lets you learn more interesting and detailed word vectors. This library is a simple Python implementation for loading, querying and training sense2vec models. For more details, check out our blog post. To explore the semantic similarities across all Reddit comments of 2015 and 2019, see the interactive demo. -
18
LlamaChat
Chat with your favourite LLaMA models in a native macOS app
Chat with your favourite LLaMA models, right on your Mac. LlamaChat is a macOS app that allows you to chat with LLaMA, Alpaca, and GPT4All models all running locally on your Mac. -
19
PandaOCR
Multifunctional OCR Image and Text Recognition
At present, the newly refactored PandaOCR.Pro professional version has been released. It is faster and more stable, with richer interfaces and easier operation. It is recommended for you to use it! The normal version will continue to be maintained, and all interfaces will be retained but no new functions will be added. The reason why the version number of the professional version starts from 5.x is that the normal version will be updated in the future, so a period of version number is... -
20
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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Convolutional Recurrent Neural Network
Convolutional Recurrent Neural Network (CRNN) for image-based sequence
Convolutional Recurrent Neural Network provides an implementation of the Convolutional Recurrent Neural Network (CRNN) architecture, a deep learning model designed for image-based sequence recognition tasks such as optical character recognition and scene text recognition. The architecture combines convolutional neural networks for extracting visual features from images with recurrent neural networks that model sequential dependencies in the extracted features. This hybrid approach allows the... -
22
SLING
A natural language frame semantics parser
...We do not yet have a full system that can extract facts from arbitrary text, but we have built a number of the subsystems needed for such a system. The SLING frame store is our basic framework for building and manipulating frame semantic graph structures. The Wiki flow pipeline can take a raw dump of Wikidata and convert this into one big frame graph. -
23
Five video classification methods
Code that accompanies my blog post outlining five video classification
...So a 41-frame video and a 500-frame video will both be reduced to 40 frames, with the 500-frame video essentially being fast-forwarded. We won’t do much preprocessing. A common preprocessing step for video classification is subtracting the mean, but we’ll keep the frames pretty raw from start to finish. -
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OWL Machine Learning
Machine learning algorithm using OWL
...Usually, these are very time-consuming and complex tasks because the features have to be manually crafted. The features are aggregated, combined or split to create features from raw data. This project makes use of ontologies to automatically generate features for the ML algorithms. The features are generated by combining the concepts and relationships that are already in the knowledge base, expressed in form of ontology. -
25
EEG Seizure Prediction
Seizure prediction from EEG data using machine learning
The Kaggle-EEG project is a machine learning solution developed for seizure prediction from EEG data, achieving 3rd place in the Kaggle/University of Melbourne Seizure Prediction competition. The repository processes EEG data to predict seizures by training machine learning models, specifically using SVM (Support Vector Machine) and RUS Boosted Tree ensemble models. The framework processes EEG data into features, trains models, and outputs predictions, handling temporal data to ensure accuracy.
