Search Results for "deep learning" - Page 14
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Showing 659 open source projects for "deep learning"
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Lightning-Hydra-Template
PyTorch Lightning + Hydra. A very user-friendly template
Convenient all-in-one technology stack for deep learning prototyping - allows you to rapidly iterate over new models, datasets and tasks on different hardware accelerators like CPUs, multi-GPUs or TPUs. A collection of best practices for efficient workflow and reproducibility. Thoroughly commented - you can use this repo as a reference and educational resource. Not fitted for data engineering - the template configuration setup is not designed for building data processing pipelines that depend on each other. ... -
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dfdx
Deep learning in Rust, with shape checked tensors and neural networks
Deep learning in Rust, with shape-checked tensors and neural networks. Ergonomics & safety focused deep learning in Rust. -
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ReplitLM
Inference code and configs for the ReplitLM model family
...The models were trained using modern deep learning techniques and large-scale GPU infrastructure to achieve strong performance in code completion and generation tasks. -
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SoftVC VITS Singing Voice Conversion
SoftVC VITS Singing Voice Conversion
SoftVC VITS Singing Voice Conversion is a deep learning project focused on singing voice conversion, allowing users to transform one voice into another while preserving melody and timing. Unlike traditional text-to-speech systems, it specializes specifically in singing scenarios and does not provide general TTS functionality. The project leverages neural network architectures derived from VITS and SoftVC research to achieve high-quality voice transformation. -
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YoloV3 Implemented in TensorFlow 2.0
YoloV3 Implemented in Tensorflow 2.0
YoloV3 Implemented in TensorFlow 2.0 is built using TensorFlow 2.0. The project provides a modern deep learning implementation of the popular YOLOv3 algorithm, which is widely used for real-time object detection in images and video streams. YOLOv3 works by dividing an image into grid regions and predicting bounding boxes and class probabilities simultaneously, allowing objects to be detected quickly and efficiently. The repository includes training scripts, inference tools, and configuration files that make it possible to train custom object detection models on user-defined datasets. ... -
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Demucs
Code for the paper Hybrid Spectrogram and Waveform Source Separation
Demucs (Deep Extractor for Music Sources) is a deep-learning framework for music source separation—extracting individual instrument or vocal tracks from a mixed audio file. The system is based on a U-Net-like convolutional architecture combined with recurrent and transformer elements to capture both short-term and long-term temporal structure. -
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SOD
An Embedded Computer Vision & Machine Learning Library
SOD is an embedded, modern cross-platform computer vision and machine learning software library that expose a set of APIs for deep-learning, advanced media analysis & processing including real-time, multi-class object detection and model training on embedded systems with limited computational resource and IoT devices. SOD was built to provide a common infrastructure for computer vision applications and to accelerate the use of machine perception in open source as well as commercial products. ... -
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ICCV2023-Paper-Code-Interpretation
ICCV2021/2019/2017 Paper/Code/Interpretation/Live Broadcast Collection
ICCV2023-Paper-Code-Interpretation is a curated repository that provides explanations and interpretations of code associated with research papers presented at the International Conference on Computer Vision (ICCV) 2023. The project focuses on helping researchers and students better understand how complex computer vision algorithms described in academic papers are implemented in practice. Many state-of-the-art research papers provide only limited implementation details, which can make... -
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Paper-with-Code-of-Wireless-comm
Paper-with-Code-of-Wireless-communication-Based-on-DL
Paper-with-Code-of-Wireless-communication-Based-on-DL is a curated repository that collects research papers and corresponding code implementations related to the application of deep learning in wireless communication systems. The project aims to help researchers and graduate students quickly find reproducible implementations of algorithms used in modern communication research. Wireless communication research has increasingly adopted deep learning techniques to address complex tasks such as channel estimation, resource allocation, signal detection, and modulation classification. ... -
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CycleGAN
Software that can generate photos from paintings
CycleGAN — in its original form — is a landmark in deep learning for image-to-image translation without paired data. Rather than requiring matching image pairs between source and target domains (which are often hard or impossible to obtain), CycleGAN learns two mappings — one from domain A to B, and another back from B to A — along with a cycle-consistency loss that encourages the round-trip to reconstruct the original image. -
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LLaMA
Inference code for Llama models
“Llama” is the repository from Meta (formerly Facebook/Meta Research) containing the inference code for LLaMA (Large Language Model Meta AI) models. It provides utilities to load pre-trained LLaMA model weights, run inference (text generation, chat, completions), and work with tokenizers. Tokenizer utilities, download scripts, shell helpers to fetch model weights with correct licensing/permissions. Includes example scripts for chat completions and text completions to show how to call the... -
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DeepH-pack
Deep neural networks for density functional theory Hamiltonian
DeepH-pack is the official implementation of the DeepH (Deep Hamiltonian) method described in the paper Deep-learning density functional theory Hamiltonian for efficient ab initio electronic-structure calculation and in the Research Briefing. DeepH-pack supports DFT results made by ABACUS, OpenMX, FHI-aims or SIESTA and will support HONPAS. -
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From Zero to Research Scientist guide
Detailed and tailored guide for undergraduate students
From-0-to-Research-Scientist-resources-guide is an open-source educational roadmap that helps learners progress from basic programming knowledge to becoming a research scientist in artificial intelligence. The repository focuses primarily on deep learning and natural language processing, providing structured guidance for individuals who want to pursue research careers in these fields. It compiles recommended courses, textbooks, tutorials, and academic resources needed to build expertise in machine learning research. The guide proposes different learning paths depending on whether the learner prefers a theoretical approach centered on mathematics or a practical approach based on hands-on experimentation. ... -
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Spektral
Graph Neural Networks with Keras and Tensorflow 2
...Spektral implements some of the most popular layers for graph deep learning. Spektral also includes lots of utilities for representing, manipulating, and transforming graphs in your graph deep learning projects. Spektral is compatible with Python 3.6 and above, and is tested on the latest versions of Ubuntu, MacOS, and Windows. Other Linux distros should work as well. The 1.0 release of Spektral is an important milestone for the library and brings many new features and improvements. -
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Horovod
Distributed training framework for TensorFlow, Keras, PyTorch, etc.
Horovod was originally developed by Uber to make distributed deep learning fast and easy to use, bringing model training time down from days and weeks to hours and minutes. With Horovod, an existing training script can be scaled up to run on hundreds of GPUs in just a few lines of Python code. Horovod can be installed on-premise or run out-of-the-box in cloud platforms, including AWS, Azure, and Databricks. -
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AI Upscaler for Blender
AI Upscaler for Blender using Real-ESRGAN
...Upscaling is done entirely on the CPU. Blender renders a low-resolution image. The Real-ESRGAN Upscaler upscales the low-resolution image to a higher-resolution image. Real-ESRGAN is a deep learning upscaler that uses neural networks to achieve excellent results by adding in detail when it upscales. -
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fastMRI
A large open dataset + tools to speed up MRI scans using ML
fastMRI is a large-scale collaborative research project by Facebook AI Research (FAIR) and NYU Langone Health that explores how deep learning can accelerate magnetic resonance imaging (MRI) acquisition without compromising image quality. By enabling reconstruction of high-fidelity MR images from significantly fewer measurements, fastMRI aims to make MRI scanning faster, cheaper, and more accessible in clinical settings. The repository provides an open-source PyTorch framework with data loaders, subsampling utilities, reconstruction models, and evaluation metrics, supporting both research reproducibility and practical experimentation. ... -
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TensorFlow Ranking
Learning to rank in TensorFlow
...Unbiased Learning-to-Rank from biased feedback data. We envision that this library will provide a convenient open platform for hosting and advancing state-of-the-art ranking models based on deep learning techniques, and thus facilitate both academic research and industrial applications. We provide a demo, with no installation required, to get started on using TF-Ranking. -
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Lightning Bolts
Toolbox of models, callbacks, and datasets for AI/ML researchers
Bolts package provides a variety of components to extend PyTorch Lightning, such as callbacks & datasets, for applied research and production. Torch ORT converts your model into an optimized ONNX graph, speeding up training & inference when using NVIDIA or AMD GPUs. We can introduce sparsity during fine-tuning with SparseML, which ultimately allows us to leverage the DeepSparse engine to see performance improvements at inference time. -
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KotlinDL
High-level Deep Learning Framework written in Kotlin
...This project aims to make Deep Learning easier for JVM and Android developers and simplify deploying deep learning models in production environments. -
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daily-paper-computer-vision
Document papers compiled daily in computer vision/deep learning
This repo is a running feed of computer-vision research, tracking new papers and notable results so practitioners can keep up without scouring multiple sites. It’s organized chronologically and often thematically, making it easy to scan what’s new in detection, segmentation, recognition, generative vision, 3D, and video understanding. The cadence is intentionally frequent, reflecting how quickly CV advances and how hard it is to maintain awareness while working full time. By aggregating... -
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TF2DeepFloorplan
TF2 Deep FloorPlan Recognition using a Multi-task Network
TF2 Deep FloorPlan Recognition using a Multi-task Network with Room-boundary-Guided Attention. Enable tensorboard, quantization, flask, tflite, docker, github actions and google colab. This repo contains a basic procedure to train and deploy the DNN model suggested by the paper 'Deep Floor Plan Recognition using a Multi-task Network with Room-boundary-Guided Attention'. It rewrites the original codes from zlzeng/DeepFloorplan into newer versions of Tensorflow and Python. -
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DIG
A library for graph deep learning research
The key difference with current graph deep learning libraries, such as PyTorch Geometric (PyG) and Deep Graph Library (DGL), is that, while PyG and DGL support basic graph deep learning operations, DIG provides a unified testbed for higher level, research-oriented graph deep learning tasks, such as graph generation, self-supervised learning, explainability, 3D graphs, and graph out-of-distribution. -
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T81 558
Applications of Deep Neural Networks
Deep learning is a group of exciting new technologies for neural networks. Through a combination of advanced training techniques and neural network architectural components, it is now possible to create neural networks that can handle tabular data, images, text, and audio as both input and output. Deep learning allows a neural network to learn hierarchies of information in a way that is like the function of the human brain. -
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Paddle Quantum
Paddle Quantum
...It has been utilized for developing several quantum machine learning applications. With the PaddlePaddle deep learning platform empowering QC, Paddle Quantum provides strong support for the scientific research community and developers in the field to easily develop QML applications. Moreover, it provides a learning platform for quantum computing enthusiasts.
