Search Results for "benchmark" - Page 12
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Showing 728 open source projects for "benchmark"
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NLP-progress
Repository to track the progress in Natural Language Processing (NLP)
...It aims to cover both traditional and core NLP tasks such as dependency parsing and part-of-speech tagging as well as more recent ones such as reading comprehension and natural language inference. The main objective is to provide the reader with a quick overview of benchmark datasets and the state-of-the-art for their task of interest, which serves as a stepping stone for further research. To this end, if there is a place where results for a task are already published and regularly maintained, such as a public leaderboard, the reader will be pointed there. -
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Yandex Tank
Load and performance benchmark tool
Yandex.Tank is an extensible open-source load testing tool for advanced Linux users which is especially good as a part of an automated load testing suite. Different load generators are supported. Evgeniy Mamchits' phantom is a very fast (100 000+ RPS) shooter written in C++ (default) JMeter is an extendable and widely known one. BFG is a Python-based generator that allows you to write your load scenarios in Python. Experimental Golang generator: pandora. Performance analytics backend... -
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SMAC
SMAC: The StarCraft Multi-Agent Challenge
SMAC (StarCraft II Multi-Agent Challenge) is a benchmark environment for cooperative multi-agent reinforcement learning (MARL), based on real-time strategy (RTS) game scenarios in StarCraft II. It allows researchers to test algorithms where multiple units (agents) must collaborate to win battles against built-in game AI opponents. SMAC provides a controlled testbed for studying decentralized execution and centralized training paradigms in MARL. -
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AutoBench
This program is a benchmark site data extraction util program
This program is a program that extracts the latest CPU, GPU, Drive and RAM performance scores and rankings from benchmark sites. The Output Data is saved as a csv, xlsx and xls file. CPU information is written by model name and score. GPU information is written by model name and score. Drive information is written by model name and score. RAM information is written by model name and score. -
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Merge PDF Files
It is a Windows library that merges standard PDFs into a final PDF
...You can send the input PDFs (by file name or by byte array) and you can have the final PDF (saved on a file or get back on a byte array). The library calls can be synchronous or asynchronous. We want to give you a benchmark, the library was used to create a PDF from single page(scanned) image by an OCR SDK (it is not included in our library, you can use any on the market): 20,000 Images (the OCR SDK creates single page PDF text searchable, running 50 threads) in 80 minutes. The size of the final PDF searchable was 800Mb. If you download the library, we provide a sample which cover all the scenarios possible (synchronous and asynchronous). -
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maskrcnn-benchmark
Fast, modular reference implementation of Instance Segmentation
Mask R-CNN Benchmark is a PyTorch-based framework that provides high-performance implementations of object detection, instance segmentation, and keypoint detection models. Originally built to benchmark Mask R-CNN and related models, it offers a clean, modular design to train and evaluate detection systems efficiently on standard datasets like COCO. -
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YouTube-8M
Starter code for working with the YouTube-8M dataset
...The repository provides a complete pipeline for video-level and frame-level modeling using TensorFlow, including data reading, model training, evaluation, and inference. It was developed to support the YouTube-8M Video Understanding Challenge (hosted on Kaggle and featured at ICCV 2019), enabling researchers and practitioners to benchmark video classification models on large-scale datasets with over millions of labeled videos. The code demonstrates how to process frame-level features, train logistic and deep learning models, evaluate them using metrics like global Average Precision (gAP) and mean Average Precision (mAP), and export trained models for MediaPipe inference. -
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List
An immutable list with unmatched performance and functional API
An immutable list with unmatched performance and a comprehensive functional API. List is a purely functional alternative to arrays. It is an implementation of a fast persistent sequence data structure. Compared to JavaScript Array List has three major benefits. List is immutable. This makes it safer and better suited for functional programming. It doesn't tempt you with an imperative API and accidental mutations won't be a source of bugs. Since List doesn't allow mutations it can be heavily... -
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I3D models trained on Kinetics
Convolutional neural network model for video classification
...This repository includes pretrained I3D models on the Kinetics dataset, with both RGB and optical flow input streams. The models have achieved state-of-the-art results on benchmark datasets such as UCF101 and HMDB51, and also won first place in the CVPR 2017 Charades Challenge. The project provides TensorFlow and Sonnet-based implementations, pretrained checkpoints, and example scripts for evaluating or fine-tuning models. It also offers sample data, including preprocessed video frames and optical flow arrays, to demonstrate how to run inference and visualize outputs. -
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benchm-ml
A benchmark of commonly used open source implementations
This repository is designed to provide a minimal benchmark framework comparing commonly used machine learning libraries in terms of scalability, speed, and classification accuracy. The focus is on binary classification tasks without missing data, where inputs can be numeric or categorical (after one-hot encoding). It targets large scale settings by varying the number of observations (n) up to millions and the number of features (after expansion) to about a thousand, to stress test different implementations. ... -
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Dopamine
Framework for prototyping of reinforcement learning algorithms
Dopamine is a research framework for fast prototyping of reinforcement learning algorithms. It aims to fill the need for a small, easily grokked codebase in which users can freely experiment with wild ideas (speculative research). This first version focuses on supporting the state-of-the-art, single-GPU Rainbow agent (Hessel et al., 2018) applied to Atari 2600 game-playing (Bellemare et al., 2013). Specifically, our Rainbow agent implements the three components identified as most important... -
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RefineNet
RefineNet: Multi-Path Refinement Networks
...It implements the architecture presented in the CVPR 2017 paper RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation and its extended version published in TPAMI 2019. The framework uses multi-path refinement and improved residual pooling to achieve high-quality segmentation results across multiple benchmark datasets. It provides trained models for datasets such as PASCAL VOC 2012, Cityscapes, NYUDv2, Person_Parts, PASCAL_Context, SUNRGBD, and ADE20k, with versions based on ResNet-101 and ResNet-152 backbones. The repository supports both single-scale and multi-scale prediction, with scripts for training, testing, and evaluating segmentation performance. ... -
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MAML-Pytorch
Elegant PyTorch implementation of paper Model-Agnostic Meta-Learning
MAML-Pytorch is a PyTorch implementation of Model-Agnostic Meta-Learning for supervised learning experiments. It focuses on reproducing and exploring the MAML approach for few-shot learning research. The repository supports MiniImagenet and Omniglot, two common benchmark datasets for meta-learning experiments. It includes separate training scripts, dataset loaders, learner components, and meta-learning logic. The project also notes that MAML can be difficult to train and presents the implementation as a practical starting point for research. Overall, it is useful for students and researchers who want to study fast adaptation, few-shot classification, and gradient-based meta-learning in PyTorch. -
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Hero
A handy, fast and powerful go template engine
Hero is a handy, fast and powerful go template engine, which pre-compiles the HTML templtes to go code. It has been used in a production environment in bthub.io. The function definition statement defines the function which represents a html file. The function defined should return one and only one parameter []byte. Extend statement states the parent template the current template extends. Include statement includes a sub-template to the current template. It works like #include in C++. Import... -
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SSD
A PyTorch Implementation of Single Shot MultiBox Detector
...The repository includes the major components needed for an object detection workflow, including training scripts, evaluation scripts, demos, and utility modules. It supports commonly used benchmark datasets such as PASCAL VOC and MS COCO, and it also provides scripts to simplify downloading and setting up those datasets. For training visibility, the project includes support for Visdom so users can monitor loss in real time through a browser-based interface. Its structure makes it useful both as a reference implementation for learning SSD and as a base for custom experimentation in detection research or practical computer vision projects. -
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HOBBIT Java SDK Example
Integrate your benchmarks and systems into the HOBBIT platform
...You may specify input parameters models for benchmark and system you are running. -
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Omniglot
Omniglot data set for one-shot learning
...The dataset provides both an image representation of each character and the time-ordered stroke coordinates ([x, y, t]) for each instance. Includes stroke data (time-sequenced coordinates) per sample. The repository is intended as a benchmark dataset in few-shot / meta-learning research, not as a plug-and-play detection or classification engine. Pre-split “background” and “evaluation” alphabets for standard benchmarking. -
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Ceras
Universal binary serializer for a wide variety of scenarios
...Ceras generally ranks at the top end of the performance spectrum, together with NetSerializer and MessagePack-CSharp. To get an idea of how Ceras performs here are the preliminary benchmark results. The resulting binary size is about the same as MessagePack-CSharp. Don't forget to tune the settings in SerializerConfig for your specific situation. Using Ceras to read/write network packets might require different settings than, lets say, saving a settings-object to a file, or persisting items/spells/monsters in a game. -
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Video Nonlocal Net
Non-local Neural Networks for Video Classification
...Efficient implementations keep memory and compute manageable so the blocks can be added without rewriting the entire backbone. The result is a practical, drop-in mechanism for upgrading purely local video models into context-aware networks with strong benchmark performance. -
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coNCePTuaL
DSL for writing communication benchmarks
coNCePTuaL is a toolset for rapidly generating portable, readable, and reproducible network-performance tests. coNCePTuaL can perform the equivalent of many pages of C code with just a few mouse clicks or lines of code in a domain-specific language. -
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BatteryCat
Battery monitoring and health tool.
Having been a Mac OSX user now for some years I appreciate the simplicity of CoconutBattery (http://www.coconut-flavour.com/), which shows you all relevant information of the battery of your laptop (iBook, PowerBook, MacBook, ...) and has one outstanding feature: you can save the current health state of the laptop's battery and thus monitor the long term development (degradation) of the battery. Since I have to work in Windows at work :-( I was looking for a similar tool in the MS world.... -
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SFD
S³FD: Single Shot Scale-invariant Face Detector, ICCV, 2017
S³FD (Single Shot Scale-invariant Face Detector) is a real-time face detection framework designed to handle faces of various sizes with high accuracy using a single deep neural network. Developed by Shifeng Zhang, S³FD introduces a scale-compensation anchor matching strategy and enhanced detection architecture that makes it especially effective for detecting small faces—a long-standing challenge in face detection research. The project builds upon the SSD framework in Caffe, with... -
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libaco
A blazing fast and lightweight C asymmetric coroutine library
...Along with the implementation of a production-ready C coroutine library, here is detailed documentation about how to implement the fastest and correct coroutine library and also with strict mathematical proof. It has no more than 700 LOC but has the full functionality which you may want from a coroutine library. The benchmark part shows that a context switch between coroutines only takes about 10 ns (in the case of the standalone stack) on the AWS c5d.large machine. User could choose to create a new coroutine with a standalone stack or with a shared stack (could be shared with others). It is extremely memory-efficient, 10,000,000 coroutines simultaneously to run cost only 2.8 GB physical memory (run with tcmalloc, each coroutine has a 120B copy-stack size configuration). ... -
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cocolib / light field suite
CUDA library for continuous optimization and light field analysis
...Implements several recent algorithms for inverse problems and image segmentation with total variation regularizers and vectorial multilabel transition costs. Also included is a suite for variational light field analysis, which ties into the HCI light field benchmark set and givens reference implementations for a number of our recently published algorithms. *** NOTE: documentation on the SourceForge page is outdated and not updated anymore, please visit http://cocolib.net ***
