Search Results for "deep" - Page 29
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Showing 1152 open source projects for "deep"
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Deep Feature Rotation Multimodal Image
Implementation of Deep Feature Rotation for Multimodal Image
Official implementation of paper Deep Feature Rotation for Multimodal Image Style Transfer [NICS'21] We propose a simple method for representing style features in many ways called Deep Feature Rotation (DFR), while still achieving effective stylization compared to more complex methods in style transfer. Our approach is a representative of the many ways of augmentation for intermediate feature embedding without consuming too much computational expense. -
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Machine Learning PyTorch Scikit-Learn
Code Repository for Machine Learning with PyTorch and Scikit-Learn
...For those who are interested in knowing what this book covers in general, I’d describe it as a comprehensive resource on the fundamental concepts of machine learning and deep learning. The first half of the book introduces readers to machine learning using scikit-learn, the defacto approach for working with tabular datasets. Then, the second half of this book focuses on deep learning, including applications to natural language processing and computer vision. -
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MeshCNN in PyTorch
Convolutional Neural Network for 3D meshes in PyTorch
MeshCNN is a deep learning framework designed specifically for processing 3D triangular mesh data using convolutional neural networks. Unlike traditional CNNs that operate on images or voxel grids, MeshCNN performs convolution operations directly on the edges of mesh structures. This design allows the model to capture geometric relationships between mesh elements while preserving the underlying topology of 3D shapes. -
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PyTTI-Notebook
PyTTI-Notebook
...PyTTI is a toolkit that facilitates image generation, animation, and manipulation using processes that could be thought of as a human artist collaborating with AI assistants. The underlying technology is complex, but you don’t need to be a deep learning expert or even know coding of any kind to use these tools. Understanding the underlying technology can be extremely helpful to leveraging it effectively, but it’s absolutely not a pre-requisite. You don’t even need a powerful computer of your own: you can play with this right now on completely free resources provided by google. ... -
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Graph4NLP
Graph4nlp is the library for the easy use of Graph Neural Networks
Graph4NLP is an easy-to-use library for R&D at the intersection of Deep Learning on Graphs and Natural Language Processing (i.e., DLG4NLP). It provides both full implementations of state-of-the-art models for data scientists and also flexible interfaces to build customized models for researchers and developers with whole-pipeline support. Built upon highly-optimized runtime libraries including DGL , Graph4NLP has both high running efficiency and great extensibility. -
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ML++
A library created to revitalize C++ as a machine learning front end
Machine learning is a vast and exiciting discipline, garnering attention from specialists of many fields. Unfortunately, for C++ programmers and enthusiasts, there appears to be a lack of support in the field of machine learning. To fill that void and give C++ a true foothold in the ML sphere, this library was written. The intent with this library is for it to act as a crossroad between low-level developers and machine learning engineers. ML++, like most frameworks, is dynamic, and... -
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MACE
Deep learning inference framework optimized for mobile platforms
Mobile AI Compute Engine (or MACE for short) is a deep learning inference framework optimized for mobile heterogeneous computing on Android, iOS, Linux and Windows devices. Runtime is optimized with NEON, OpenCL and Hexagon, and Winograd algorithm is introduced to speed up convolution operations. The initialization is also optimized to be faster. Chip-dependent power options like big.LITTLE scheduling, Adreno GPU hints are included as advanced APIs. -
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Voice Cloning App
A Python/Pytorch app for easily synthesising human voices
A Python/Pytorch app for easily synthesizing human voices. If you are using a language other than English you can add it to the app. Firstly, you'll need to find a deep speech model for your language by going to coqui. You'll then need to download the model.pbmm and alphabet.txt files for your language. Requires Windows 10 or Ubuntu 20.04+ operating system, 5GB+ Disk space, and NVIDIA GPU with at least 4GB of memory & driver version 456.38+ (optional). Automatic dataset generation (with support for subtitles and audiobooks) Additional language support. ... -
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What happens when
What happens when you type google into your browser and press enter?
What happens when is a large collaborative documentation-style project that aims to answer in exhaustive detail the canonical interview/thought experiment question, “What happens when you type google into your browser and press Enter?” Rather than giving a high-level overview, the repository tries to break down every step in the process, from low-level events (keyboard press, OS events, keyboard interrupts), through OS-level handling (keyboard scan codes, key events), parsing, DNS lookup,... -
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Face Mask Detection
Face Mask Detection system based on computer vision and deep learning
Face Mask Detection system based on computer vision and deep learning using OpenCV and Tensorflow/Keras. Face Mask Detection System built with OpenCV, Keras/TensorFlow using Deep Learning and Computer Vision concepts in order to detect face masks in static images as well as in real-time video streams. Amid the ongoing COVID-19 pandemic, there are no efficient face mask detection applications which are now in high demand for transportation means, densely populated areas, residential districts, large-scale manufacturers and other enterprises to ensure safety. ... -
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Big Sleep
A simple command line tool for text to image generation
A simple command line tool for text to image generation, using OpenAI's CLIP and a BigGAN. Ryan Murdock has done it again, combining OpenAI's CLIP and the generator from a BigGAN! This repository wraps up his work so it is easily accessible to anyone who owns a GPU. You will be able to have the GAN dream-up images using natural language with a one-line command in the terminal. User-made notebook with bug fixes and added features, like google drive integration. Images will be saved to... -
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DeepDanbooru
AI based multi-label girl image classification system
DeepDanbooru is a deep learning system designed to automatically tag anime-style images using neural networks trained on datasets derived from the Danbooru imageboard. The project focuses on multi-label image classification, where a model predicts multiple descriptive tags that represent visual elements in an image. These tags may include characters, styles, clothing, emotions, or other attributes associated with anime artwork. -
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Awesome Explainable Graph Reasoning
A collection of research papers and software related to explainability
A collection of research papers and software related to explainability in graph machine learning. Deep learning methods are achieving ever-increasing performance on many artificial intelligence tasks. A major limitation of deep models is that they are not amenable to interpretability. This limitation can be circumvented by developing post hoc techniques to explain the predictions, giving rise to the area of explainability. Recently, explainability of deep models on images and texts has achieved significant progress. ... -
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Music Source Separation
Separate audio recordings into individual sources
Music Source Separation is a PyTorch-based open-source implementation for the task of separating a music (or audio) recording into its constituent sources — for example isolating vocals, instruments, bass, accompaniment, or background from a mixed track. It aims to give users the ability to take any existing song and decompose it into separate stems (vocals, accompaniment, etc.), or to train custom separation models on their own datasets (e.g. for speech enhancement, instrument isolation, or... -
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Test Manager plugin for Trac
A Trac plugin manage the quality process
A Trac plugin to create Test Cases, organize them in catalogs and track their execution status and outcome. Take a look at the Web Page on trac-hacks: http://trac-hacks.org/wiki/TestManagerForTracPlugin -
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Deep Learning 500 Questions
500 Questions on Deep Learning using a question-and-answer format
DeepLearning-500-questions is a comprehensive handbook that compiles 500 important questions on deep learning, curated to serve as a valuable reference for AI engineer interviews and self-study. Edited by Tan Jiyong with contributions from Guo Zizhao, Li Jian, and Dian Songyi, the book systematically covers both theoretical foundations and practical applications of deep learning. The first sections focus on essential mathematics, machine learning basics, and deep learning foundations, establishing the groundwork for more advanced topics. ... -
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SVoice (Speech Voice Separation)
We provide a PyTorch implementation of the paper Voice Separation
SVoice is a PyTorch-based implementation of Facebook Research’s study on speaker voice separation as described in the paper “Voice Separation with an Unknown Number of Multiple Speakers.” This project presents a deep learning framework capable of separating mixed audio sequences where several people speak simultaneously, without prior knowledge of how many speakers are present. The model employs gated neural networks with recurrent processing blocks that disentangle voices over multiple computational steps, while maintaining speaker consistency across output channels. ... -
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VQGAN-CLIP web app
Local image generation using VQGAN-CLIP or CLIP guided diffusion
VQGAN-CLIP has been in vogue for generating art using deep learning. Searching the r/deepdream subreddit for VQGAN-CLIP yields quite a number of results. Basically, VQGAN can generate pretty high-fidelity images, while CLIP can produce relevant captions for images. Combined, VQGAN-CLIP can take prompts from human input, and iterate to generate images that fit the prompts. Thanks to the generosity of creators sharing notebooks on Google Colab, the VQGAN-CLIP technique has seen widespread circulation. ... -
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deeplinks.js
Simple deep links to any selection of text on your website
deeplinks.js allows people to easily link directly to any text selection on your website. Here's an example of what one of these links looks like. It's intended mostly for blogs and other such websites, but it's relatively agnostic to the environment it's running in. People can select text on the site as the normally would, and when they do, the fragment identifier (the thing that comes after the # in the URL) changes. If they want to share the text they have selected, they simply copy the... -
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Machine Learning & Deep Learning
machine learning and deep learning tutorials, articles
Machine Learning & Deep Learning Tutorials is an open-source repository that provides practical tutorials demonstrating how to implement machine learning and deep learning models using popular frameworks such as TensorFlow and PyTorch. The project focuses on helping learners understand machine learning through hands-on coding examples rather than purely theoretical explanations. -
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MoCo v3
PyTorch implementation of MoCo v3
MoCo v3 is a PyTorch reimplementation of Momentum Contrast v3 (MoCo v3), Facebook Research’s state-of-the-art self-supervised learning framework for visual representation learning using ResNet and Vision Transformer (ViT) backbones. Originally developed in TensorFlow for TPUs, this version faithfully reproduces the paper’s results on GPUs while offering an accessible and scalable PyTorch interface. MoCo v3 introduces improvements for training self-supervised ViTs by combining contrastive... -
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Trax
Deep learning with clear code and speed
Trax is an end-to-end library for deep learning that focuses on clear code and speed. It is actively used and maintained in the Google Brain team. Run a pre-trained Transformer, create a translator in a few lines of code. Features and resources, API docs, where to talk to us, how to open an issue and more. Walkthrough, how Trax works, how to make new models and train on your own data. -
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Detectron2
Next-generation platform for object detection and segmentation
Detectron2 is Facebook AI Research's next generation software system that implements state-of-the-art object detection algorithms. It is a ground-up rewrite of the previous version, Detectron, and it originates from maskrcnn-benchmark. It is powered by the PyTorch deep learning framework. Includes more features such as panoptic segmentation, Densepose, Cascade R-CNN, rotated bounding boxes, PointRend, DeepLab, etc. Can be used as a library to support different projects on top of it. We'll open source more research projects in this way. It trains much faster. Models can be exported to TorchScript format or Caffe2 format for deployment. ... -
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ReinforcementLearningAnIntroduction.jl
Julia code for the book Reinforcement Learning An Introduction
...One of our main goals is to help users understand the basic concepts of reinforcement learning from an engineer's perspective. Once you have grasped how different components are organized, you're ready to explore a wide variety of modern deep reinforcement learning algorithms in ReinforcementLearningZoo.jl. -
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DnCNN
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN
This repository implements DnCNN (“Deep CNN Denoiser”) from the paper “Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising”. DnCNN is a feedforward convolutional neural network that learns to predict the residual noise (i.e. noise map) from a noisy input image, which is then subtracted to yield a clean image. This formulation allows efficient denoising, supports blind Gaussian noise (i.e. unknown noise levels), and can be extended to related tasks like image super-resolution or JPEG deblocking in some variants. ...
