Search Results for "python windows" - Page 16
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Showing 747 open source projects for "python windows"
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Tensor Puzzles
Solve puzzles. Improve your pytorch
Tensor Puzzles is an interactive collection of 21 exercises for learning tensor programming in PyTorch and NumPy. Each puzzle asks the learner to recreate a familiar array operation from first principles. Solutions must fit on one short line and use only a restricted set of indexing, arithmetic, comparison, and broadcasting tools. Standard convenience functions such as sum, view, squeeze, and take are intentionally prohibited. This constraint encourages a deeper understanding of shapes,... -
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snntorch
Deep and online learning with spiking neural networks in Python
snntorch is a deep learning library that enables researchers and developers to build and train spiking neural networks using the PyTorch framework. Spiking neural networks are biologically inspired models that communicate through discrete spike events rather than continuous activation values, making them closer to how neurons operate in the brain. The library extends PyTorch’s tensor computation capabilities to support gradient-based learning for networks composed of spiking neurons. This... -
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PyTextRank
Python implementation of TextRank algorithms
PyTextRank is a Python implementation of TextRank as a spaCy pipeline extension, for graph-based natural language work -- and related knowledge graph practices. -
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PyDenseCRF
Python wrapper to Philipp Krähenbühl's dense (fully connected) CRFs
PyDenseCRF is a Python library that provides a wrapper around the implementation of fully connected Conditional Random Fields (CRFs) developed by Philipp Krähenbühl and Vladlen Koltun. The project allows developers and researchers to integrate Dense CRF inference into Python-based machine learning pipelines, particularly for computer vision tasks such as image segmentation and labeling. Conditional Random Fields are probabilistic graphical models used to model contextual relationships... -
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Foolbox
Python toolbox to create adversarial examples
Foolbox: Fast adversarial attacks to benchmark the robustness of machine learning models in PyTorch, TensorFlow, and JAX. Foolbox 3 is built on top of EagerPy and runs natively in PyTorch, TensorFlow, and JAX. Foolbox provides a large collection of state-of-the-art gradient-based and decision-based adversarial attacks. Catch bugs before running your code thanks to extensive type annotations in Foolbox. Foolbox is a Python library that lets you easily run adversarial attacks against machine... -
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snorkel
A system for quickly generating training data with weak supervision
The Snorkel team is now focusing their efforts on Snorkel Flow, an end-to-end AI application development platform based on the core ideas behind Snorkel. The Snorkel project started at Stanford in 2016 with a simple technical bet: that it would increasingly be the training data, not the models, algorithms, or infrastructure, that decided whether a machine learning project succeeded or failed. Given this premise, we set out to explore the radical idea that you could bring mathematical and... -
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scikit-learn-videos
Jupyter notebooks from the scikit-learn video series
scikit-learn-videos repository accompanies a video tutorial series designed to teach machine learning using Python’s scikit-learn library. It provides the Jupyter notebooks used in each lesson so learners can reproduce the demonstrations and experiment with the code themselves. The series introduces fundamental machine learning concepts such as classification, regression, model evaluation, feature engineering, and cross-validation using clear examples and real datasets. Each video... -
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OneFlow
OneFlow is a deep learning framework designed to be user-friendly
OneFlow is a deep learning framework designed to be user-friendly, scalable and efficient. An extension for OneFlow to target third-party compiler, such as XLA, TensorRT and OpenVINO etc.CUDA runtime is statically linked into OneFlow. OneFlow will work on a minimum supported driver, and any driver beyond. For more information. Distributed performance (efficiency) is the core technical difficulty of the deep learning framework. OneFlow focuses on performance improvement and heterogeneous... -
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Autodistill
Images to inference with no labeling
Autodistill uses big, slower foundation models to train small, faster supervised models. Using autodistill, you can go from unlabeled images to inference on a custom model running at the edge with no human intervention in between. You can use Autodistill on your own hardware, or use the Roboflow hosted version of Autodistill to label images in the cloud. -
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TensorFlow Privacy
Library for training machine learning models with privacy for data
Library for training machine learning models with privacy for training data. This repository contains the source code for TensorFlow Privacy, a Python library that includes implementations of TensorFlow optimizers for training machine learning models with differential privacy. The library comes with tutorials and analysis tools for computing the privacy guarantees provided. -
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TensorFlow Hub
A library for transfer learning by reusing parts of TensorFlow models
TensorFlow Hub is a repository that provides a library and platform for publishing, discovering, and reusing pre-trained machine learning models built with TensorFlow. The project enables developers to integrate high-quality models into their applications without needing to train them from scratch. Through TensorFlow Hub, researchers and practitioners can share reusable model components such as image classifiers, text embedding models, and object detection networks. These models can be... -
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Computer vision projects
computer vision projects | Fun AI projects related to computer vision
Computer vision projects is an open-source collection of computer vision projects and experiments that demonstrate practical applications of modern AI techniques in image processing, robotics, and real-time visual analysis. The repository includes multiple demonstration systems implemented using languages such as Python and C++, covering topics ranging from object detection to embedded vision systems. Many of the projects illustrate how computer vision algorithms can interact with hardware... -
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AI-Aimbot
CS2, Valorant, Fortnite, APEX, every game
AI-Aimbot is a computer vision project that demonstrates how artificial intelligence can be used to automatically identify and target opponents in video games. The system uses an object detection model based on the YOLOv5 architecture to detect human-shaped characters in gameplay screenshots or video frames. Once a target is identified, the program automatically adjusts the player’s aim toward the detected target, effectively automating the aiming process in first-person shooter games. The... -
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Sonnet
TensorFlow-based neural network library
Sonnet is a neural network library built on top of TensorFlow designed to provide simple, composable abstractions for machine learning research. Sonnet can be used to build neural networks for various purposes, including different types of learning. Sonnet’s programming model revolves around a single concept: modules. These modules can hold references to parameters, other modules and methods that apply some function on the user input. There are a number of predefined modules that already... -
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SSD in PyTorch 1.0
High quality, fast, modular reference implementation of SSD in PyTorch
This repository implements SSD (Single Shot MultiBox Detector). The implementation is heavily influenced by the projects ssd.pytorch, pytorch-ssd and maskrcnn-benchmark. This repository aims to be the code base for research based on SSD. Multi-GPU training and inference: We use DistributedDataParallel, you can train or test with arbitrary GPU(s), the training schema will change accordingly. Add your own modules without pain. We abstract backbone, Detector, BoxHead, BoxPredictor, etc. You can... -
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PromptTools
Open-source tools for prompt testing and experimentation
Welcome to prompttools created by Hegel AI! This repo offers a set of open-source, self-hostable tools for experimenting with, testing, and evaluating LLMs, vector databases, and prompts. The core idea is to enable developers to evaluate using familiar interfaces like code, notebooks, and a local playground. -
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BackgroundMattingV2
Real-Time High-Resolution Background Matting
Official repository for the paper Real-Time High-Resolution Background Matting. Our model requires capturing an additional background image and produces state-of-the-art matting results at 4K 30fps and HD 60fps on an Nvidia RTX 2080 TI GPU. -
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MMDeploy
OpenMMLab Model Deployment Framework
MMDeploy is an open-source deep learning model deployment toolset. It is a part of the OpenMMLab project. Models can be exported and run in several backends, and more will be compatible. All kinds of modules in the SDK can be extended, such as Transform for image processing, Net for Neural Network inference, Module for postprocessing and so on. Install and build your target backend. ONNX Runtime is a cross-platform inference and training accelerator compatible with many popular ML/DNN... -
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CLIP-as-service
Embed images and sentences into fixed-length vectors
CLIP-as-service is a low-latency high-scalability service for embedding images and text. It can be easily integrated as a microservice into neural search solutions. Serve CLIP models with TensorRT, ONNX runtime and PyTorch w/o JIT with 800QPS[*]. Non-blocking duplex streaming on requests and responses, designed for large data and long-running tasks. Horizontally scale up and down multiple CLIP models on single GPU, with automatic load balancing. Easy-to-use. No learning curve, minimalist... -
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MMEditing
MMEditing is a low-level vision toolbox based on PyTorch
MMEditing is an open-source toolbox for low-level vision. It supports various tasks. MMEditing is a low-level vision toolbox based on PyTorch, supporting super-resolution, inpainting, matting, video interpolation, etc. We decompose the editing framework into different components and one can easily construct a customized editor framework by combining different modules. The toolbox directly supports popular and contemporary inpainting, matting, super-resolution and generation tasks. The... -
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DPM-Solver
Fast ODE Solver for Diffusion Probabilistic Model Sampling
DPM-Solver is a machine learning research implementation focused on accelerating the sampling process in diffusion probabilistic models used for generative AI tasks. Diffusion models are powerful generative systems capable of producing high-quality images and other data, but traditional sampling methods often require hundreds or thousands of computational steps. The project introduces a specialized numerical solver designed to approximate the diffusion process using a small number of... -
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TensorFlow Addons
Useful extra functionality for TensorFlow 2.x maintained by SIG-addons
TensorFlow Addons is a repository of contributions that conform to well-established API patterns but implement new functionality not available in core TensorFlow. TensorFlow natively supports a large number of operators, layers, metrics, losses, and optimizers. However, in a fast-moving field like ML, there are many interesting new developments that cannot be integrated into core TensorFlow (because their broad applicability is not yet clear, or it is mostly used by a smaller subset of the... -
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Taylorplot_Neptune
Creation of a Taylorplot for several machine learning models
Here we present the lines of code for creating a taylor plot with python to display several machine learning models. We show the solution for displaying 10 models, but the list and number can be changed simply by modifying the sample list. -
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TensorFlow.NET
.NET Standard bindings for Google's TensorFlow for developing models
TensorFlow.NET (TF.NET) provides a .NET Standard binding for TensorFlow. It aims to implement the complete Tensorflow API in C# which allows .NET developers to develop, train and deploy Machine Learning models with the cross-platform .NET Standard framework. TensorFlow.NET has built-in Keras high-level interface and is released as an independent package TensorFlow.Keras. SciSharp STACK's mission is to bring popular data science technology into the .NET world and to provide .NET developers... -
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SageMaker Inference Toolkit
Serve machine learning models within a Docker container
Serve machine learning models within a Docker container using Amazon SageMaker. Amazon SageMaker is a fully managed service for data science and machine learning (ML) workflows. You can use Amazon SageMaker to simplify the process of building, training, and deploying ML models. Once you have a trained model, you can include it in a Docker container that runs your inference code. A container provides an effectively isolated environment, ensuring a consistent runtime regardless of where the...
