Search Results for "performance" - Page 34
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Showing 1009 open source projects for "performance"
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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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Wally
Distributed Stream Processing
...Create a dependable and resilient distributed computing framework. Take care of the complexities of distributed computing "plumbing," allowing developers to focus on their business logic. Provide high-performance & low-latency data processing. Be portable and deploy easily (i.e., run on-prem or any cloud). Manage in-memory state for the application. Allow applications to scale as needed, even when they are live and up-and-running. The primary API for Wally is written in Pony. Wally applications are written using this Pony API. -
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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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Nebula Kernel
Nebula Kernel, My Personal Custom Kernel geared towards Performance.
High Performance Kernel that also maintains stability and battery drain to a min. -
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Improved GAN
Code for the paper "Improved Techniques for Training GANs"
...It provides implementations of experiments conducted on datasets such as MNIST, SVHN, CIFAR-10, and ImageNet. The project focuses on demonstrating enhanced training methods for Generative Adversarial Networks, addressing stability and performance issues that were common in earlier GAN models. The repository includes training scripts, evaluation methods, and pretrained configurations for reproducing experimental results. By offering structured experiments across multiple datasets, it allows researchers to study and replicate the improvements described in the paper. Although the project is archived and not actively maintained, it remains a reference point in the history of GAN research, influencing subsequent model training approaches. -
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Evolution Strategies Starter
Code for the paper "Evolution Strategies.."
...The repository demonstrates how to scale Evolution Strategies (ES) for reinforcement learning tasks using a master-worker architecture, where the master node broadcasts parameters to multiple workers, and the workers return performance results after evaluation. This approach allows for efficient parallelization and robustness against worker termination, making it ideal for distributed execution on Amazon EC2 spot instances. The codebase supports building custom AMIs with Packer, integrates with MuJoCo for simulation-based experiments, and includes scripts for launching and managing large-scale runs. ... -
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Catalyst
An Algorithmic Trading Library for Crypto-Assets in Python
...It builds on top of Zipline, extending that ecosystem to support crypto exchanges and high-resolution historical data (daily and minute bars). Users can express strategies in Python, run backtests against historical price data, and analyze performance through built-in metrics and analytics to evaluate profitability, risk, and behavior under different market conditions. Beyond backtesting, Catalyst was designed to support live trading on multiple crypto exchanges such as Binance, Bitfinex, Bittrex, and Poloniex, bridging simulation and production within the same framework. The library includes a rich set of examples, Docker and conda configurations, and integration points for community resources like forums and Discord for sharing strategies and troubleshooting. -
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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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TensorImage
Image classification library for easily training and deploying models
(Visit our github repository at https://github.com/TensorImage/tensorimage for more information) TensorImage is and open source package for image classification. It has a wide range of data augmentation operations that can be performed over training data to prevent overfitting and increase testing accuracy. TensorImage is easy to use and manage as all files, trained models and data are organized within a workspace directory, which you can change at any time in the configuration file,... -
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10
Mixup-CIFAR10
mixup: Beyond Empirical Risk Minimization
...This repository implements mixup for the CIFAR-10 dataset, showcasing its effectiveness in improving generalization, stability, and calibration of neural networks. The approach acts as a regularizer, encouraging linear behavior in the feature space between samples, which helps reduce overfitting and enhance performance on unseen data. -
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fooltrader
Quant framework for stock
...Its applicable objects include quantitative traders, teachers, and students majoring in finance, people interested in economic data, programmers, and people who like freedom and the spirit of exploration. You could write the Strategy using an event-driven or time walkway and view and analyze the performance in a uniform way. -
12
anaGo
Bidirectional LSTM-CRF and ELMo for Named-Entity Recognition
anaGo is a Python library for sequence labeling(NER, PoS Tagging,...), implemented in Keras. anaGo can solve sequence labeling tasks such as named entity recognition (NER), part-of-speech tagging (POS tagging), semantic role labeling (SRL) and so on. Unlike traditional sequence labeling solver, anaGo doesn't need to define any language-dependent features. Thus, we can easily use anaGo for any language. In anaGo, the simplest type of model is the Sequence model. Sequence model includes... -
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mAP
Evaluates the performance of your neural net for object recognition
In practice, a higher mAP value indicates a better performance of your neural net, given your ground truth and set of classes. The performance of your neural net will be judged using the mAP criteria defined in the PASCAL VOC 2012 competition. We simply adapted the official Matlab code into Python (in our tests they both give the same results). First, your neural net detection-results are sorted by decreasing confidence and are assigned to ground-truth objects. -
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Vaex
Out-of-Core hybrid Apache Arrow/NumPy DataFrame for Python
...Vaex uses memory mapping, zero memory copy policy and lazy computations for best performance (no memory wasted). Cut development cut development time by 80%. Your prototype is your solution. Create automatic pipelines for any model. -
15
DIGITS
Deep Learning GPU training system
...DIGITS can be used to rapidly train the highly accurate deep neural network (DNNs) for image classification, segmentation and object detection tasks. DIGITS simplifies common deep learning tasks such as managing data, designing and training neural networks on multi-GPU systems, monitoring performance in real-time with advanced visualizations, and selecting the best performing model from the results browser for deployment. DIGITS is completely interactive so that data scientists can focus on designing and training networks rather than programming and debugging. DIGITS is available as a free download to the members of the NVIDIA Developer Program. ... -
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darc
Durham Adaptive-optics Real-time Controller
darc, the Durham Adaptive optics Real-time Controller. For documentation or darctalk client only, select "View all files". For the latest bleeding-edge version, please use: git clone git://git.code.sf.net/p/darc2/code darc (no password required) (this changed May 2013 due to a sourceforge update). If you use darc, please cite with: Basden, A and Myers, R, MNRAS Vol 242, page 1483, 2012 -
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...The latter is necessary for parameter estimation when individual-level data is available. A critical aspect of this coupling is the implementation of an algorithm that would allow the performance of population-based statistical analysis. Although novelty is encouraged in regards to the algorithm, proposals could entertain approaches such as: a. Implementing the non-linear mixed effects theory, b. Maximum log-likelihood algorithms (stiff, non-stiff Ordinary Differential Equation solving methods with linearization)
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haipproxy
Distributed proxy IP pool for web crawlers using Scrapy and Redis
...It automatically crawls proxy resources from the internet and aggregates them into a centralized pool that can be accessed by distributed spiders and scraping systems. It is built using Python and relies on Scrapy for high-performance crawling while Redis is used for data storage, communication, and task coordination between components. It includes crawlers that discover proxy servers, validators that test proxy availability and performance, and schedulers that manage crawling and validation tasks. HAipproxy aims to maintain a high availability proxy pool with low latency so that scraping frameworks can rotate proxies efficiently and avoid blocking during large-scale data collection. ... -
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Pyben-nio
Simple python network benchmark that you can ride on!
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TEACUP
TCP Experiment Automation Controlled Using Python
TEACUP automates many aspects of running TCP performance experiments in a specially-constructed physical testbed. TEACUP enables repeatable testing of different TCP algorithms over a range of emulated network path conditions, bottleneck rate limits and bottleneck queuing disciplines. TEACUP utilises a text-based configuration file to define experiments as combinations of parameters specifying desired network path and end host conditions. -
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Intel neon
Intel® Nervana™ reference deep learning framework
neon is Intel's reference deep learning framework committed to best performance on all hardware. Designed for ease of use and extensibility. See the new features in our latest release. We want to highlight that neon v2.0.0+ has been optimized for much better performance on CPUs by enabling Intel Math Kernel Library (MKL). The DNN (Deep Neural Networks) component of MKL that is used by neon is provided free of charge and downloaded automatically as part of the neon installation. ... -
22
House3D
A Realistic and Rich 3D Environment
...Each environment includes fully labeled 3D objects, allowing agents to perceive and interact with their surroundings through multiple sensory modalities including RGB images, depth maps, semantic segmentation masks, and top-down maps. The simulator is optimized for high-performance rendering, achieving thousands of frames per second to enable efficient large-scale training of RL agents. House3D has served as the foundation for several influential research projects such as RoomNav (for concept-based navigation) and Embodied Question Answering (EQA). -
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Tangent
Source-to-source debuggable derivatives in pure Python
...Tangent is useful to researchers and students who not only want to write their models in Python, but also read and debug automatically-generated derivative code without sacrificing speed and flexibility. Tangent works on a large and growing subset of Python, provides extra autodiff features other Python ML libraries don't have, has reasonable performance, and is compatible with TensorFlow and NumPy. -
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Pulsar framework
Event driven concurrent framework for Python
Event-driven concurrent framework for Python. Pulsar's goal is to provide an easy way to build scalable network programs. In the Hello world! webserver example above, many client connections can be handled concurrently. Pulsar tells the operating system (through epoll or select) that it should be notified when a new connection is made, and then it goes to sleep. Pulsar uses the asyncio module from the standard python library and it can be configured to run in multi-processing mode. The http... -
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cnn-benchmarks
Benchmarks for popular CNN models
...It is particularly useful for testing GPUs and optimizing deep learning workloads, as it highlights bottlenecks and performance differences across setups. The repository includes scripts for running benchmarks on various architectures and datasets, making it easy to gather comparative metrics. By simplifying performance evaluation, it helps developers make informed decisions about model design and hardware selection. Overall, cnn-benchmarks is a practical tool for performance analysis in deep learning workflows.
