Search Results for "simple-xml"
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Showing 8 open source projects for "simple-xml"
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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.
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Host LLMs in Production With On-Demand GPUsDeploy your model, get an endpoint, pay only for compute time. No GPU provisioning or infrastructure management required.
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Label Studio
Label Studio is a multi-type data labeling and annotation tool
...Use ML models to pre-label and optimize the process. Label Studio is an open-source data labeling tool. It lets you label data types like audio, text, images, videos, and time series with a simple and straightforward UI and export to various model formats. It can be used to prepare raw data or improve existing training data to get more accurate ML models. The frontend part of Label Studio app lies in the frontend/ folder and written in React JSX. Multi-user labeling sign up and login, when you create an annotation it's tied to your account. ... -
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DeepDetect
Deep Learning API and Server in C++14 support for Caffe, PyTorch
...While the Open Source Deep Learning Server is the core element, with REST API, and multi-platform support that allows training & inference everywhere, the Deep Learning Platform allows higher level management for training neural network models and using them as if they were simple code snippets. Ready for applications of image tagging, object detection, segmentation, OCR, Audio, Video, Text classification, CSV for tabular data and time series. Neural network templates for the most effective architectures for GPU, CPU, and Embedded devices. Training in a few hours and with small data thanks to 25+ pre-trained models. ... -
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ML.NET
Open source and cross-platform machine learning framework for .NET
...ML.NET lets you re-use all the knowledge, skills, code, and libraries you already have as a .NET developer so that you can easily integrate machine learning into your web, mobile, desktop, games, and IoT apps. ML.NET offers Model Builder (a simple UI tool) and ML.NET CLI to make it super easy to build custom ML Models. These tools use Automated ML (AutoML), a cutting edge technology that automates the process of building best performing models for your Machine Learning scenario. All you have to do is load your data, and AutoML takes care of the rest of the model building process. ... -
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...It simulates key processes of our minds, such as organizing data into concepts and categories, planning actions based on their predicted outcome, and communication. LifeAI was designed to be simple, but powerful and flexible enough to have many applications.
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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.
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Flashlight library
A C++ standalone library for machine learning
Flashlight is a fast, flexible machine learning library written entirely in C++ by Facebook AI Research and the creators of Torch, TensorFlow, Eigen, and Deep Speech. Native support in C++ and simple extensibility make Flashlight a powerful research framework that's hackable to its core and enables fast iteration on new experimental setups and algorithms with little unopinionated and without sacrificing performance. In a single repository, Flashlight provides apps for research across multiple domains. Flashlight can be broken down into several components as described above. ... -
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DETR
End-to-end object detection with transformers
PyTorch training code and pretrained models for DETR (DEtection TRansformer). We replace the full complex hand-crafted object detection pipeline with a Transformer, and match Faster R-CNN with a ResNet-50, obtaining 42 AP on COCO using half the computation power (FLOPs) and the same number of parameters. Inference in 50 lines of PyTorch. What it is. Unlike traditional computer vision techniques, DETR approaches object detection as a direct set prediction problem. It consists of a set-based... -
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TensorNets
High level network definitions with pre-trained weights in TensorFlow
...For example, all the inception variants are implemented as about 500 lines of code in TensorNets while 2000+ lines in official TensorFlow models. Reproducibility. You can always reproduce the original results with simple APIs including feature extractions. -
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ObjectDetector
Car Detection,Face Detectiom,Object Detection
Machine learning: This project is used for training new object like Car,Motor Cycle and so on and we use this model(xml file) for detecting in images.In this project we use viola jones algorithm.
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