Search Results for "feature extraction of charcters" - Page 2
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Showing 107 open source projects for "feature extraction of charcters"
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ESPnet
End-to-end speech processing toolkit
ESPnet is a comprehensive end-to-end speech processing toolkit covering a wide spectrum of tasks, including automatic speech recognition (ASR), text-to-speech (TTS), speech translation (ST), speech enhancement, speaker diarization, and spoken language understanding. It uses PyTorch as its deep learning engine and adopts a Kaldi-style data processing pipeline for features, data formats, and experimental recipes. This combination allows researchers to leverage modern neural architectures while... -
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Baymax toOls for x64dbg
the feature code extraction and search plug-in for x64dbg
BaymaxTools is a feature code extraction and search plug-in for x64dbg debugger. it is convenient to extract the signature of the specified instruction and the number of occurrences of the signature in the search module. -
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pwwAutoVision
Non-standard automated vision software
The only software that integrates vision and motion, as well as interfaces and reports, is developed with zero code and comes with online debugging. Link：https://pan.baidu.com/s/1vsTptn_pvtbK2sDhWVCZJg code：1234 -
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NanaZip
The 7-Zip derivative intended for the modern Windows experience
NanaZip is an open source file archiver intended for the modern Windows experience, forked from the source code of well-known open source file archiver 7-Zip. Source code is hosted at https://github.com/M2Team/NanaZip. -
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GeoDMA
Geographic feature extraction and data mining
GeoDMA is a plugin for TerraView software, used for geographical data mining. With a single image, the user can perform segmentation, attributes extraction, normalization and classification. -
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affe
apple fruit feature extractor (affe)
The apple fruit features extractor (affe) is a tool for feature extraction in apple phenotyping under the GPL 3 license https://opensource.org/license/gpl-3-0 It works on data of the Fruitphenobox https://www.researchsquare.com/article/rs-2632704/v1 Developed and tested by Norbert Kirchgessner, ETH Zurich using Matlab 2018b as source code and compiled For the generation of fact sheets, some R skripts written by Marius Hodel, Agroscope Wädenswil, Switzerland, are included. ... -
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FFmpeg Batch AV Converter
Free all in one audio/video ffmpeg batch encoder
FFmpeg Batch AV Converter is a free universal audio and video encoder for Windows and Linux (via Wine), that allows to use the full potential of ffmpeg command line with a few mouse clicks in a convenient GUI with drag and drop, progress information. Some fancy wizards make things easy for non-experts. Thanks to its multi-file encoding feature, it may be the fastest a/v batch encoder available, since it maximizes system resources usage by launching as many simultaneous processes up to... -
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Honeyview
Fast and lightweight image viewer
Honeyview is a fast and lightweight image viewer that supports a wide range of image formats, including popular ones like JPG, PNG, and BMP, as well as less common formats such as PSD and RAW. Designed for users who need an efficient tool for viewing and organizing images, Honeyview provides a clean and intuitive interface with high-speed performance. It also supports viewing images within compressed archives, such as ZIP or RAR files, without the need for extraction. Additionally, Honeyview... -
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PoseidonQ - AI/ML Based QSAR Modeling
ML based QSAR Modelling And Translation of Model to Deployable WebApps
- This Software was made with an intention to make QSAR/QSPR development more efficient and reproducible. - Published in ACS, Journal of Chemical Information and Modeling . Link : https://pubs.acs.org/doi/10.1021/acs.jcim.4c02372 - Simple to use and no compromise on essential features necessary to make reliable QSAR models. - From Generating Reliable ML Based QSAR Models to Developing Your Own QSAR WebApp. For any feedback or queries, contact kabeermuzammil614@gmail.com - Available on... -
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OpenKM Document Management - DMS
Document Management System and Content Management System
OpenKM Community Edition is a free Document Management System (DMS) that helps businesses control the production, storage, management and distribution of electronic documents, boosting effectiveness and productivity. It integrates document management, collaboration and advanced search into one easy-to-use solution, including administration tools for user roles, access control, security levels, activity logs and automation setup. With OpenKM Community Edition you can: Collect information... -
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pattern_classification
A collection of tutorials and examples for solving machine learning
...The project aims to help learners understand the process of building predictive models by presenting structured explanations and practical examples. It includes notebooks and guides that demonstrate data preprocessing, feature extraction, model training, and evaluation techniques used in machine learning workflows. The repository also covers algorithms such as Bayesian classification, logistic regression, neural networks, clustering methods, and ensemble models. In addition to algorithm tutorials, the project contains supplementary resources such as dataset collections, visualization examples, and links to recommended books and talks. ... -
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Multiple Excel Sheet Combiner Free
Multiple Excel combiner including sheets with large data
...License Expiry Check: The application checks the license expiry date, ensuring that users are informed if their license has expired before performing any operations. 3. Data Combining: Users can combine data from multiple Excel files into a single dataset. This feature is useful for data consolidation and analysis. 4. Starting Row Selection: Users can specify the starting row for data extraction from each sheet, allowing flexibility in handling different data structures. 5. Output Format Options: The application provides options to save the combined data in various formats, including CSV, Excel, and text files, giving users flexibility in how they want to store and share the data. 6. ... -
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Ominis-Osint
Ominis OSINT: Secure Web-history Search
Ominis OSINT Secure Web-history Search stands as a premier solution for digital reconnaissance, offering unparalleled capabilities in gathering, analyzing, and interpreting publicly available information sourced from diverse online platforms. With its comprehensive suite of features, Ominis equips users to navigate through the expansive digital landscape with precision and efficiency, enabling the extraction of valuable insights from a myriad of sources. -
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SageMaker Experiments Python SDK
Experiment tracking and metric logging for Amazon SageMaker notebooks
...There is no relationship between Trial Components such as ordering. Trial Component: A description of a single step in a machine learning workflow. For example data cleaning, feature extraction, model training, model evaluation, etc. -
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audioFlux
A library for audio and music analysis, feature extraction.
audioflux is a deep learning tool library for audio and music analysis, feature extraction. It supports dozens of time-frequency analysis transformation methods and hundreds of corresponding time-domain and frequency-domain feature combinations. It can be provided to deep learning networks for training, and is used to study various tasks in the audio field such as Classification, Separation, Music Information Retrieval(MIR) and ASR etc. -
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LSTMs for Human Activity Recognition
Human Activity Recognition example using TensorFlow on smartphone
LSTM-Human-Activity-Recognition is a machine learning project that demonstrates how recurrent neural networks can be used to recognize human activities from sensor data. The repository implements a deep learning model based on Long Short-Term Memory (LSTM) networks to classify physical activities using time-series data collected from wearable sensors. The project uses the well-known Human Activity Recognition dataset derived from smartphone accelerometer and gyroscope signals. Through the... -
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FUNseq
Cell segmentation/tracking for determing aggresive cancer phenotypes
This page shows the cell segmentation/tracking program for FUNseq pipeline. Identifying a sparse subset of cancer cells from a large heterogeneous population, based on aggressive phenotypes (like invasive migration, multipolar divisions, or asymmetric lineage development) is challenging. Also, such aberration identification is critical, as cells exhibiting these characteristics are linearly correlated with poor prognosis. A high-throughput screening microscope has been developed in our... -
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Hugging Face Transformer
CPU/GPU inference server for Hugging Face transformer models
Optimize and deploy in production Hugging Face Transformer models in a single command line. At Lefebvre Dalloz we run in-production semantic search engines in the legal domain, in the non-marketing language it's a re-ranker, and we based ours on Transformer. In that setup, latency is key to providing a good user experience, and relevancy inference is done online for hundreds of snippets per user query. Most tutorials on Transformer deployment in production are built over Pytorch and FastAPI.... -
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VAD
Voice activity detection (VAD) toolkit including DNN, bDNN, LSTM
This repository is a voice activity detection (VAD) toolkit that implements multiple models (DNN, bDNN, LSTM, ACAM) for detecting speech versus non-speech in audio. It also provides a recorded dataset in varied real-world settings (e.g. bus stop, construction site, park, room) with ground truth labeling. Acoustic feature extraction (multi-resolution cochleagram, MRCG). Post-processing modules (e.g. smoothing, thresholds). The toolkit supports both MATLAB and Python/TensorFlow components (for feature extraction, classification, postprocessing). Acoustic feature extraction (multi-resolution cochleagram, MRCG). Provided real-world dataset with manual annotations. -
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SynWrite
Advanced text editor for programmers and Notepad replacement.
SynWrite is a free source code editor, HTML editor, and Notepad replacement for Windows systems. It is a full-featured alternative not only to simple Notepad, but also to professional editors. -
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cnn-for-image-retrieval is a research-oriented project that demonstrates the use of convolutional neural networks (CNNs) for image retrieval tasks. The repository provides implementations of CNN-based methods to extract feature representations from images and use them for similarity-based retrieval. It focuses on applying deep learning techniques to improve upon traditional handcrafted descriptors by learning features directly from data. The code includes training and evaluation scripts that...
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DeepCluster
Deep Clustering for Unsupervised Learning of Visual Features
...This alternating “cluster & train” scheme helps the model gradually discover meaningful structure without labels. DeepCluster was one of the early successes in unsupervised visual feature learning, demonstrating that clustering-based reformulation can rival supervised baselines for many downstream tasks. The repository includes code for feature extraction, clustering, training loops, and evaluation benchmarks like linear probes. Because of its simplicity and modular design, DeepCluster has inspired many later methods. -
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DeText
A Deep Neural Text Understanding Framework
DeText is a Deep Text understanding framework for NLP-related ranking, classification, and language generation tasks. It leverages semantic matching using deep neural networks to understand member intents in search and recommender systems. As a general NLP framework, DeText can be applied to many tasks, including search & recommendation ranking, multi-class classification and query understanding tasks. -
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PyTracking
Visual tracking library based on PyTorch
A general python framework for visual object tracking and video object segmentation, based on PyTorch. Official implementation of the RTS (ECCV 2022), ToMP (CVPR 2022), KeepTrack (ICCV 2021), LWL (ECCV 2020), KYS (ECCV 2020), PrDiMP (CVPR 2020), DiMP (ICCV 2019), and ATOM (CVPR 2019) trackers, including complete training code and trained models. -
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Image Super-Resolution (ISR)
Super-scale your images and run experiments with Residual Dense
The goal of this project is to upscale and improve the quality of low-resolution images. This project contains Keras implementations of different Residual Dense Networks for Single Image Super-Resolution (ISR) as well as scripts to train these networks using content and adversarial loss components. Docker scripts and Google Colab notebooks are available to carry training and prediction. Also, we provide scripts to facilitate training on the cloud with AWS and Nvidia-docker with only a few...
