Search Results for "red-files"
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Showing 15 open source projects for "red-files"
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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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Video-subtitle-extractor
A GUI tool for extracting hard-coded subtitle (hardsub) from videos
...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. Use local OCR recognition, no need to set up and call any API, and do not need to access online OCR services such as Baidu and Ali to complete text recognition locally. Support GPU acceleration, after GPU acceleration, you can get higher accuracy and faster extraction speed. (CLI version) No need for users to manually set the subtitle area, the project automatically detects the subtitle area through the text detection model. ... -
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Netron
Visualizer for neural network, deep learning, machine learning models
...Netron has experimental support for TensorFlow, PyTorch, TorchScript, OpenVINO, Torch, Arm NN, BigDL, Chainer, CNTK, Deeplearning4j, MediaPipe, ML.NET, scikit-learn, TensorFlow.js. There is an extense variety of sample model files to download or open using the browser version. It is supported by macOS, Windows, Linux, Python Server and browser. -
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OpenVINO Training Extensions
Trainable models and NN optimization tools
...When ote_cli is installed in the virtual environment, you can use the ote command line interface to perform various actions for templates related to the chosen task type, such as running, training, evaluating, exporting, etc. ote train trains a model (a particular model template) on a dataset and saves results in two files. ote optimize optimizes a pre-trained model using NNCF or POT depending on the model format. NNCF optimization used for trained snapshots in a framework-specific format. POT optimization used for models exported in the OpenVINO IR format. -
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Open Model Zoo
Pre-trained Deep Learning models and demos
...It includes hundreds of models covering object detection, classification, segmentation, pose estimation, speech recognition, text-to-speech, and more, many of which are already converted into formats optimized for inference on CPUs, GPUs, VPUs, and other accelerators supported by OpenVINO. In addition to model files, Open Model Zoo provides demo applications that show realistic usage patterns and help developers quickly prototype and understand inference pipelines in C++, Python, or via the OpenCV Graph API. Tools in the repository also help automate model downloads and other tasks, making it easier to incorporate these models into production systems or custom solutions. -
Automate contact and company data extractionGenerate leads at scale without building or maintaining scrapers. Use 10,000+ ready-made tools that handle authentication, pagination, and anti-bot protection. Pull data from business directories, social profiles, and public sources, then export to your CRM or database via API. Schedule recurring extractions, enrich existing datasets, and integrate with your workflows.
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Raster Vision
Open source framework for deep learning satellite and aerial imagery
...Raster Vision allows engineers to quickly and repeatably configure pipelines that go through core components of a machine learning workflow: analyzing training data, creating training chips, training models, creating predictions, evaluating models, and bundling the model files and configuration for easy deployment. The input to a Raster Vision pipeline is a set of images and training data, optionally with Areas of Interest (AOIs) that describe where the images are labeled. The output of a Raster Vision pipeline is a model bundle that allows you to easily utilize models in various deployment scenarios. -
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Deep Learning Models
A collection of various deep learning architectures, models, and tips
...Examples span fundamental architectures—MLPs, CNNs, RNN/Transformers—and practical tasks like image classification or text modeling. Reproducible training scripts and configuration files make it straightforward to rerun experiments or adapt them to your own datasets. The repo often pairs implementations with notes on design choices and trade-offs, turning it into both a toolbox and a learning resource. It’s suitable for students, researchers prototyping ideas, and practitioners who want clean baselines before adding complexity. -
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GluonTS
Probabilistic time series modeling in Python
GluonTS is a Python package for probabilistic time series modeling, focusing on deep learning based models. GluonTS requires Python 3.6 or newer, and the easiest way to install it is via pip. We train a DeepAR-model and make predictions using the simple "airpassengers" dataset. The dataset consists of a single time-series, containing monthly international passengers between the years 1949 and 1960, a total of 144 values (12 years * 12 months). We split the dataset into train and test parts,... -
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Lightning-Hydra-Template
PyTorch Lightning + Hydra. A very user-friendly template
...Hydra, a framework for elegantly configuring complex applications. The key feature is the ability to dynamically create a hierarchical configuration by composition and override it through config files and the command line. -
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Catalyst
Accelerated deep learning R&D
Catalyst is a PyTorch framework for accelerated Deep Learning research and development. It allows you to write compact but full-featured Deep Learning pipelines with just a few lines of code. With Catalyst you get a full set of features including a training loop with metrics, model checkpointing and more, all without the boilerplate. Catalyst is focused on reproducibility, rapid experimentation, and codebase reuse so you can break the cycle of writing another regular train loop and make... -
Grafana: The open and composable observability platformGrafana is the open source analytics & monitoring solution for every database.
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10
The fastai book
The fastai book, published as Jupyter Notebooks
...These notebooks are used for a MOOC and form the basis of this book, which is currently available for purchase. It does not have the same GPL restrictions that are on this repository. The code in the notebooks and python .py files is covered by the GPL v3 license; see the LICENSE file for details. The remainder (including all markdown cells in the notebooks and other prose) is not licensed for any redistribution or change of format or medium, other than making copies of the notebooks or forking this repo for your own private use. No commercial or broadcast use is allowed. ... -
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Deep Daze
Simple command line tool for text to image generation
Simple command-line tool for text to image generation using OpenAI's CLIP and Siren (Implicit neural representation network). In true deep learning fashion, more layers will yield better results. Default is at 16, but can be increased to 32 depending on your resources. Technique first devised and shared by Mario Klingemann, it allows you to prime the generator network with a starting image, before being steered towards the text. Simply specify the path to the image you wish to use, and... -
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English2Cards
English Learning software
English2Cards is a smart English learning program designed to improve your listening & speaking skills without Internet connection using great learning files. The program repeatedly reviews the educational cards at different times so that you can remember the new words and phrases that you have learned and be able to use them in real conversations easily and without thinking. During learning you can remember the words in the text and also get the translation, pronunciation and examples of the new words and review them later. ... -
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Delta ML
Deep learning based natural language and speech processing platform
...DELTA has been used for developing several state-of-the-art algorithms for publications and delivering real production to serve millions of users. It helps you to train, develop, and deploy NLP and/or speech models. Use configuration files to easily tune parameters and network structures. What you see in training is what you get in serving: all data processing and features extraction are integrated into a model graph. Text classification, named entity recognition, question and answering, text summarization, etc. Uniform I/O interfaces and no changes for new models. -
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Image classification models for Keras
Keras code and weights files for popular deep learning models
All architectures are compatible with both TensorFlow and Theano, and upon instantiation the models will be built according to the image dimension ordering set in your Keras configuration file at ~/.keras/keras.json. For instance, if you have set image_dim_ordering=tf, then any model loaded from this repository will get built according to the TensorFlow dimension ordering convention, "Width-Height-Depth". Pre-trained weights can be automatically loaded upon instantiation (weights='imagenet'... -
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ConvNetJS
Deep learning in Javascript to train convolutional neural networks
...If you would like to add features to the library, you will have to change the code in src/ and then compile the library into the build/ directory. The compilation script simply concatenates files in src/ and then minifies the result.
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