Search Results for "performance" - Page 5
Sort By:
Showing 188 open source projects for "performance"
View related business solutions-
MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
-
Our Free Plans just got better! | Auth0You asked, we delivered! Auth0 is excited to expand our Free and Paid plans to include more options so you can focus on building, deploying, and scaling applications without having to worry about your security. Auth0 now, thank yourself later.
-
1
PyDenseCRF
Python wrapper to Philipp Krähenbühl's dense (fully connected) CRFs
...By implementing a fully connected CRF model with Gaussian edge potentials, the library enables efficient inference across all pixel pairs in an image rather than only local neighborhoods. The Python wrapper is implemented using Cython, allowing high-performance CRF computations while maintaining a Python-friendly interface for experimentation and development. -
2
Foolbox
Python toolbox to create adversarial examples
Foolbox: Fast adversarial attacks to benchmark the robustness of machine learning models in PyTorch, TensorFlow, and JAX. Foolbox 3 is built on top of EagerPy and runs natively in PyTorch, TensorFlow, and JAX. Foolbox provides a large collection of state-of-the-art gradient-based and decision-based adversarial attacks. Catch bugs before running your code thanks to extensive type annotations in Foolbox. Foolbox is a Python library that lets you easily run adversarial attacks against machine... -
3
AI-Aimbot
CS2, Valorant, Fortnite, APEX, every game
AI-Aimbot is a computer vision project that demonstrates how artificial intelligence can be used to automatically identify and target opponents in video games. The system uses an object detection model based on the YOLOv5 architecture to detect human-shaped characters in gameplay screenshots or video frames. Once a target is identified, the program automatically adjusts the player’s aim toward the detected target, effectively automating the aiming process in first-person shooter games. The... -
4
SSD in PyTorch 1.0
High quality, fast, modular reference implementation of SSD in PyTorch
This repository implements SSD (Single Shot MultiBox Detector). The implementation is heavily influenced by the projects ssd.pytorch, pytorch-ssd and maskrcnn-benchmark. This repository aims to be the code base for research based on SSD. Multi-GPU training and inference: We use DistributedDataParallel, you can train or test with arbitrary GPU(s), the training schema will change accordingly. Add your own modules without pain. We abstract backbone, Detector, BoxHead, BoxPredictor, etc. You can... -
Fully Managed MySQL, PostgreSQL, and SQL ServerCloud SQL handles your database ops end to end, so you can focus on your app.
-
5
Alink
Alink is the Machine Learning algorithm platform based on Flink
...It provides a wide variety of ready-to-use ML algorithms for tasks like classification, regression, clustering, recommendation, and more. Written in Java and Scala, Alink is suitable for enterprise-grade big data applications where performance and scalability are crucial. It supports model training, evaluation, and deployment in real-time environments and integrates seamlessly into Alibaba’s cloud ecosystem. -
6
spaGO
Self-contained Machine Learning and Natural Language Processing lib
...A good place to start is by looking at the implementation of built-in neural models, such as the LSTM. Except for a few linear algebra operations written in assembly for optimal performance (a bit of copying from Gonum), it's straightforward Go code, so you don't have to worry. -
7
FEDML Open Source
The unified and scalable ML library for large-scale training
...Highly integrated with TensorOpera open source library, TensorOpera AI provides holistic support of three interconnected AI infrastructure layers: user-friendly MLOps, a well-managed scheduler, and high-performance ML libraries for running any AI jobs across GPU Clouds. A typical workflow is shown in the figure above. When a developer wants to run a pre-built job in Studio or Job Store, TensorOperaLaunch swiftly pairs AI jobs with the most economical GPU resources, and auto-provisions, and effortlessly runs the job, eliminating complex environment setup and management. -
8
fastquant
Backtest and optimize your ML trading strategies with only 3 lines
...By automating data retrieval, strategy evaluation, and result visualization, the library reduces the barrier to entry for individuals interested in quantitative finance. The project also supports optimization workflows that allow users to search for parameter combinations that improve trading strategy performance. -
9
LLM Applications
A comprehensive guide to building RAG-based LLM applications
LLM Applications is a practical reference repository that demonstrates how to build production-grade applications powered by large language models. The project focuses particularly on Retrieval-Augmented Generation architectures, which combine language models with external knowledge sources to improve accuracy and reliability. It provides step-by-step guidance for constructing systems that ingest documents, split them into chunks, generate embeddings, index them in vector databases, and... -
Custom VMs From 1 to 96 vCPUs With 99.95% UptimeLive migration and automatic failover keep workloads online through maintenance. One free e2-micro VM every month.
-
10
Lightning-Hydra-Template
PyTorch Lightning + Hydra. A very user-friendly template
...Not fitted for data engineering - the template configuration setup is not designed for building data processing pipelines that depend on each other. PyTorch Lightning, a lightweight PyTorch wrapper for high-performance AI research. Think of it as a framework for organizing your PyTorch code. Hydra, a framework for elegantly configuring complex applications. The key feature is the ability to dynamically create a hierarchical configuration by composition and override it through config files and the command line. -
11
fe4ml-zh
Feature Engineering for Machine Learning
fe4ml-zh is an open-source project that provides a Chinese translation and structured documentation of the book Feature Engineering for Machine Learning. The repository aims to make advanced feature engineering concepts accessible to a broader audience by translating the content and organizing it into readable documentation and code examples. Feature engineering is a critical component of machine learning pipelines because it determines how raw data is transformed into features that... -
12
SIG Rust
Rust language bindings for TensorFlow
...Rather than replacing TensorFlow itself, it acts as an integration layer that connects Rust applications to the TensorFlow C API. The repository is designed for developers who want Rust’s performance, safety, and systems programming strengths while still accessing TensorFlow’s machine learning capabilities. It includes setup instructions that explain how the crate can automatically download or compile the required TensorFlow shared libraries, which lowers the barrier to getting started. The project also supports environments where TensorFlow is already installed, giving developers more flexibility in how they configure their systems. ... -
13
MMOCR
OpenMMLab Text Detection, Recognition and Understanding Toolbox
...The modular design of MMOCR enables users to define their own optimizers, data preprocessors, and model components such as backbones, necks and heads as well as losses. Please refer to Getting Started for how to construct a customized model. The toolbox provides a comprehensive set of utilities which can help users assess the performance of models. It includes visualizers which allow visualization of images, ground truths as well as predicted bounding boxes, and a validation tool for evaluating checkpoints. -
14
Lightning Bolts
Toolbox of models, callbacks, and datasets for AI/ML researchers
...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. -
15
AB3DMOT
Official Python Implementation for "3D Multi-Object Tracking
...This relatively simple design allows the tracker to achieve very high processing speeds while maintaining competitive tracking accuracy. The project also introduces new evaluation metrics specifically designed for assessing performance in 3D tracking benchmarks. The framework has been evaluated on widely used datasets such as KITTI and nuScenes and demonstrates strong performance compared with more complex tracking systems. -
16
picoGPT
An unnecessarily tiny implementation of GPT-2 in NumPy
picoGPT is a minimal implementation of the GPT-2 language model designed to demonstrate how transformer-based language models work at a conceptual level. The repository focuses on educational clarity rather than production performance, implementing the core components of the GPT architecture in a concise and readable way. It allows users to understand how tokenization, transformer layers, attention mechanisms, and autoregressive text generation operate in modern large language models. The project uses a small amount of code to illustrate the essential mathematical operations involved in training and running a transformer-based neural network. ... -
17
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 are of varying sizes and cover a variety graph machine learning tasks, including prediction of node, link, and graph properties. ... -
18
...Its aim is to make machine learning possible for novice users by means of a simple, consistent API, while simultaneously exploiting C++ language features to provide maximum performance and flexibility for expert users. * More info + downloads: https://mlpack.org * Git repo: https://github.com/mlpack/mlpack
-
19
T81 558
Applications of Deep Neural Networks
...This course will introduce the student to classic neural network structures, Convolution Neural Networks (CNN), Long Short-Term Memory (LSTM), Gated Recurrent Neural Networks (GRU), General Adversarial Networks (GAN) and reinforcement learning. Application of these architectures to computer vision, time series, security, natural language processing (NLP), and data generation will be covered. High-Performance Computing (HPC) aspects will demonstrate how deep learning can be leveraged both on graphical processing units (GPUs), as well as grids. -
20
Merlion
A Machine Learning Framework for Time Series Intelligence
...It provides an end-to-end machine learning framework that includes loading and transforming data, building and training models, post-processing model outputs, and evaluating model performance. It supports various time series learning tasks, including forecasting, anomaly detection, and change point detection for both univariate and multivariate time series. This library aims to provide engineers and researchers a one-stop solution to rapidly develop models for their specific time series needs, and benchmark them across multiple time series datasets. -
21
LSTMs for Human Activity Recognition
Human Activity Recognition example using TensorFlow on smartphone
LSTM-Human-Activity-Recognition is a machine learning project that demonstrates how recurrent neural networks can be used to recognize human activities from sensor data. The repository implements a deep learning model based on Long Short-Term Memory (LSTM) networks to classify physical activities using time-series data collected from wearable sensors. The project uses the well-known Human Activity Recognition dataset derived from smartphone accelerometer and gyroscope signals. Through the... -
22
Pigo
Fast face detection, pupil/eyes localization
...The reason why Pigo has been developed is because almost all of the currently existing solutions for face detection in the Go ecosystem are purely bindings to some C/C++ libraries like OpenCV or dlib, but calling a C program through cgo introduces huge latencies and implies a significant trade-off in terms of performance. Also, in many cases installing OpenCV on various platforms is cumbersome. -
23
BEVFormer
Implementation of BEVFormer, a camera-only framework
...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. -
24
Coqui STT
The deep learning toolkit for speech-to-text
...With text-to-speech, experience the immediacy of script-to-performance. Cast from a wide selection of high-quality, directable, emotive voices or clone a voice to suit your needs. With Coqui text-to-speech, production times go from months to minutes. -
25
SGX-Full-OrderBook-Tick-Data-Trading
Providing the solutions for high-frequency trading (HFT) strategies
SGX-Full-OrderBook-Tick-Data-Trading-Strategy is an open-source research project focused on modeling high-frequency financial market behavior using machine learning techniques. The repository analyzes tick-level order book data from the Singapore Exchange and attempts to capture the dynamics of limit order book movements. By extracting features such as order depth ratios and price movement indicators, the system trains machine learning models to predict short-term market changes. Several...
