Search Results for "state-thread" - Page 13
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Showing 535 open source projects for "state-thread"
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Hyperformer
Hypergraph Transformer for Skeleton-based Action Recognition
...More recently, a limitation of GCNs is identified, i.e., the topology is fixed after training. To relax such a restriction, Self-Attention (SA) mechanism has been adopted to make the topology of GCNs adaptive to the input, resulting in the state-of-the-art hybrid models. Concurrently, attempts with plain Transformers have also been made, but they still lag behind state-of-the-art GCN-based methods due to the lack of structural prior. -
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megawake
Remotely turn on devices connected to your home network
...More details available in: https://en.wikipedia.org/wiki/Wake-on-LAN For this program to work you have to: 1-Connect to the same network as the device you want to wake. 2-Enable Wake on Lan (WoL) feature on the device you want to wake. 3-Make sure the device is turned off or in sleep state. System requirements for WoL: 1-Smart TV must be manufactured in 2012 or newer. 2-Windows PC must be Windows 95 or newer. 3-Wake on Lan supported BIOS (Firmware) -
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VALL-E
PyTorch implementation of VALL-E (Zero-Shot Text-To-Speech)
...VALL-E emerges in-context learning capabilities and can be used to synthesize high-quality personalized speech with only a 3-second enrolled recording of an unseen speaker as an acoustic prompt. Experiment results show that VALL-E significantly outperforms the state-of-the-art zero-shot TTS system in terms of speech naturalness and speaker similarity. In addition, we find VALL-E could preserve the speaker's emotion and acoustic environment of the acoustic prompt in synthesis. -
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OGB
Benchmark datasets, data loaders, and evaluators for graph machine
The Open Graph Benchmark (OGB) is a collection of realistic, large-scale, and diverse benchmark datasets for machine learning on graphs. OGB datasets are automatically downloaded, processed, and split using the OGB Data Loader. The model performance can be evaluated using the OGB Evaluator in a unified manner. OGB is a community-driven initiative in active development. We expect the benchmark datasets to evolve. OGB provides a diverse set of challenging and realistic benchmark datasets that... -
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XiaomiFirmwareUpdater
Provides you with firmware packages for all Xiaomi devices (Backup)
...Use our website to get latest updates once it's out. Website: https://xiaomifirmwareupdater.com/ Supported Devices: https://xiaomifirmwareupdater.com/supported/firmware/ XDA Support Thread: https://forum.xda-developers.com/android/software-hacking/devices-xiaomi-firmware-updater-t3741446 Source code: https://github.com/XiaomiFirmwareUpdater/ Telegram Channel: https://t.me/XiaomiFirmwareUpdater -
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OptiMate
Libraries for optimizing AI models, inference speed, and GPU usage
...Its modules help developers automatically apply optimization techniques that better align AI models with the capabilities of the underlying hardware such as GPUs and CPUs. One of the core components, Speedster, focuses on accelerating model inference by applying state of the art optimization techniques to increase performance while lowering operational costs. Another component, Nos, targets infrastructure optimization by improving GPU utilization in Kubernetes clusters through dynamic partitioning and elastic resource quotas. -
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scanless
Online port scan scraper
...It supports services such as ipfingerprints, spiderip, standingtech, viewdns, and yougetsignal. scanless can return raw scan output as well as parsed results that include port, state, service, and protocol details. The repository is archived and no longer maintained, so it is best treated as a legacy utility for authorized testing, research, or reference use. -
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PARL
A high-performance distributed training framework
PARL is a scalable reinforcement learning framework built on top of PaddlePaddle. It focuses on modularity and ease of use, supporting distributed training and a variety of RL algorithms. -
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ClassyVision
An end-to-end PyTorch framework for image and video classification
Classy Vision is a PyTorch-based framework designed for large-scale training and deployment of state-of-the-art image and video classification models. Developed by Facebook Research, it serves as an end-to-end system that simplifies the process of training at scale, reducing redundancy and friction in moving from research to production. Unlike traditional computer vision libraries that focus solely on modular components, Classy Vision provides a complete and unified framework, featuring distributed training, reproducible experiments, and flexible configuration tools. ... -
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Sockeye
Sequence-to-sequence framework, focused on Neural Machine Translation
Sockeye is an open-source sequence-to-sequence framework for Neural Machine Translation built on PyTorch. It implements distributed training and optimized inference for state-of-the-art models, powering Amazon Translate and other MT applications. For a quickstart guide to training a standard NMT model on any size of data, see the WMT 2014 English-German tutorial. If you are interested in collaborating or have any questions, please submit a pull request or issue. You can also send questions to sockeye-dev-at-amazon-dot-com. ... -
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Visdom
A tool for creating, organizing, and sharing data visualizations
...The UI begins as a blank slate, you can populate it with plots, images, and text. These appear in windows that you can drag, drop, resize, and destroy. The windows live in envs and the state of envs is stored across sessions. You can download the content of windows, including your plots in svg. -
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Classic HWUT
Software Unit Tests (Language Independent Approach)
...In particular for C, HWUT supports make file generation using 'sos' and 'sols' modes. Remote control-able function stubs may be generated using the 'stub' mode. Test cases can be generated using the 'gen' mode, and state machine walkers by the 'sm_walker' mode. Over the last decade HWUT has matured towards a full fledged unit test tool for a wide variety of applications. -
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NÜWA - Pytorch
Implementation of NÜWA, attention network for text to video synthesis
Implementation of NÜWA, state of the art attention network for text-to-video synthesis, in Pytorch. It also contains an extension into video and audio generation, using a dual decoder approach. It seems as though a diffusion-based method has taken the new throne for SOTA. However, I will continue on with NUWA, extending it to use multi-headed codes + hierarchical causal transformer. -
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ImageAI
A python library built to empower developers
ImageAI is an easy-to-use Computer Vision Python library that empowers developers to easily integrate state-of-the-art Artificial Intelligence features into their new and existing applications and systems. It is used by thousands of developers, students, researchers, tutors and experts in corporate organizations around the world. You will find features supported, links to official documentation as well as articles on ImageAI. ImageAI is widely used around the world by professionals, students, research groups and businesses. ... -
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FairScale
PyTorch extensions for high performance and large scale training
...It introduced Fully Sharded Data Parallel (FSDP) style techniques that shard model parameters, gradients, and optimizer states across ranks to fit bigger models into the same memory budget. The library also provides pipeline parallelism, activation checkpointing, mixed precision, optimizer state sharding (OSS), and auto-wrapping policies that reduce boilerplate in complex distributed setups. Its components are modular, so teams can adopt just the sharding optimizer or the pipeline engine without rewriting their training loop. FairScale puts emphasis on correctness and debuggability, offering hook points, logging, and reference examples for common trainer patterns. ... -
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Karate Club
An API Oriented Open-source Python Framework for Unsupervised Learning
Karate Club is an unsupervised machine learning extension library for NetworkX. Karate Club consists of state-of-the-art methods to do unsupervised learning on graph-structured data. To put it simply it is a Swiss Army knife for small-scale graph mining research. First, it provides network embedding techniques at the node and graph level. Second, it includes a variety of overlapping and non-overlapping community detection methods. Implemented methods cover a wide range of network science (NetSci, Complenet), data mining (ICDM, CIKM, KDD), artificial intelligence (AAAI, IJCAI) and machine learning (NeurIPS, ICML, ICLR) conferences, workshops, and pieces from prominent journals. -
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Python library to automate X11. Simulate mouse movement and mouse clicks, keystrokes, and manipulate windows from Python. Alter the state of caps lock, num lock, and scroll lock. Also access the X11 clipboard. The C library would be useful for those wishing to automate X from another language as well.
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dirsearch
Web path scanner
...To use multiple wordlists, you can separate your wordlists with commas. Example: wordlist1.txt,wordlist2.txt. Default values for dirsearch flags can be edited in the configuration file: default.conf. The thread number (-t | --threads) reflects the number of separated brute force processes. And so the bigger the thread number is, the faster dirsearch runs. By default, the number of threads is 30, but you can increase it if you want to speed up the progress. -
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EnCodec
State-of-the-art deep learning based audio codec
Encodec is a neural audio codec developed by Meta for high-fidelity, low-bitrate audio compression using end-to-end deep learning. Unlike traditional codecs (like MP3 or Opus), Encodec uses a learned quantizer and decoder to reconstruct complex waveforms with remarkable accuracy at bitrates as low as 1.5 kbps. It employs a convolutional encoder–decoder architecture trained with perceptual loss functions that optimize for human auditory quality rather than raw waveform distance. The model can... -
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MMTracking
OpenMMLab Video Perception Toolbox
...It is built upon MMDetection that we can capitalize any detector only through modifying the configs. All operations run on GPUs. The training and inference speeds are faster than or comparable to other implementations. We reproduce state-of-the-art models and some of them even outperform the official implementations. -
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BEVFormer
Implementation of BEVFormer, a camera-only framework
...To aggregate spatial information, we design spatial cross-attention that each BEV query extracts the spatial features from the regions of interest across camera views. For temporal information, we propose temporal self-attention to recurrently fuse the history BEV information. Our approach achieves the new state-of-the-art 56.9\% in terms of NDS metric on the nuScenes \texttt{test} set, which is 9.0 points higher than previous best arts and on par with the performance of LiDAR-based baseline. -
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Facexlib
FaceXlib aims at providing ready-to-use face-related functions
facexlib is a PyTorch-based library providing ready-to-use face-related functions, including detection, alignment, recognition, and more. It integrates state-of-the-art open-source methods for various face processing tasks. -
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ConvNeXt
Code release for ConvNeXt model
...It achieves competitive or superior results on ImageNet and downstream datasets while being easier to deploy and train than transformers. The repository provides pretrained models, training recipes, and ablation studies demonstrating how incremental design choices collectively yield state-of-the-art performance. -
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Official YOLOv7
YOLOv7: Trainable bag-of-freebies sets new state-of-the-art
YOLOv7 is the official implementation of the paper “YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors.” It is a PyTorch-based object detection project focused on high speed and strong accuracy for real-time computer vision. The repository provides model definitions, training scripts, testing tools, inference examples, pretrained weights, and deployment-oriented materials. YOLOv7 introduced training-time improvements that raise accuracy without increasing inference cost, which is why the project became important in real-time detection research. ... -
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TheoDORE
Theoretical Density, Orbital Relaxation and Exciton analysis
The TheoDORE (Theoretical Density, Orbital Relaxation and Exciton analysis) package is a general purpose program suite for the analysis of excited states obtained from quantum chemical excited state calculations. Wavefunction analysis is based on state and transition density matrices, which provides a unified formalism applicable independent of the wavefunction model. TheoDORE is interfaced to a number of quantum chemical programs (Q-Chem, Columbus, Turbomole, ...) and can be used for a wide range of excited state methods (MR-CI, ADC, CC, TDDFT). ...
