Showing 7 open source projects for "json-cpp"

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    Keep company data safe with Chrome Enterprise

    Protect your business with AI policies and data loss prevention in the browser

    Make AI work your way with Chrome Enterprise. Block unapproved sites and set custom data controls that align with your company's policies.
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  • Incredable is the first DLT-secured platform that allows you to save time, eliminate errors, and ensure your organization is compliant all in one place. Icon
    Incredable is the first DLT-secured platform that allows you to save time, eliminate errors, and ensure your organization is compliant all in one place.

    For healthcare Providers and Facilities

    Incredable streamlines and simplifies the complex process of medical credentialing for hospitals and medical facilities, helping you save valuable time, reduce costs, and minimize risks. With Incredable, you can effortlessly manage all your healthcare providers and their credentials within a single, unified platform. Our state-of-the-art technology ensures top-notch data security, giving you peace of mind.
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  • 1
    LayoutParser

    LayoutParser

    A Unified Toolkit for Deep Learning Based Document Image Analysis

    With the help of state-of-the-art deep learning models, Layout Parser enables extracting complicated document structures using only several lines of code. This method is also more robust and generalizable as no sophisticated rules are involved in this process. A complete instruction for installing the main Layout Parser library and auxiliary components. Learn how to load DL Layout models and use them for layout detection. The full list of layout models currently available in Layout Parser....
    Downloads: 0 This Week
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  • 2
    igel

    igel

    Machine learning tool that allows you to train and test models

    A delightful machine learning tool that allows you to train/fit, test, and use models without writing code. The goal of the project is to provide machine learning for everyone, both technical and non-technical users. I sometimes needed a tool sometimes, which I could use to fast create a machine learning prototype. Whether to build some proof of concept, create a fast draft model to prove a point or use auto ML. I find myself often stuck writing boilerplate code and thinking too much about...
    Downloads: 0 This Week
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  • 3
    Opyrator

    Opyrator

    Turns your machine learning code into microservices with web API

    ...Deploy and access your services via HTTP API or interactive UI. Seamlessly export your services into portable, shareable, and executable files or Docker images. Opyrator builds on open standards - OpenAPI, JSON Schema, and Python type hints - and is powered by FastAPI, Streamlit, and Pydantic. It cuts out all the pain for productizing and sharing your Python code - or anything you can wrap into a single Python function. An Opyrator-compatible function is required to have an input parameter and return value based on Pydantic models. The input and output models are specified via type hints. ...
    Downloads: 0 This Week
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  • 4
    CapsGNN

    CapsGNN

    A PyTorch implementation of "Capsule Graph Neural Network"

    A PyTorch implementation of "Capsule Graph Neural Network" (ICLR 2019). The high-quality node embeddings learned from the Graph Neural Networks (GNNs) have been applied to a wide range of node-based applications and some of them have achieved state-of-the-art (SOTA) performance. However, when applying node embeddings learned from GNNs to generate graph embeddings, the scalar node representation may not suffice to preserve the node/graph properties efficiently, resulting in sub-optimal graph...
    Downloads: 0 This Week
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  • Award-Winning Medical Office Software Designed for Your Specialty Icon
    Award-Winning Medical Office Software Designed for Your Specialty

    Succeed and scale your practice with cloud-based, data-backed, AI-powered healthcare software.

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  • 5
    Semantic Segmentation in PyTorch

    Semantic Segmentation in PyTorch

    Semantic segmentation models, datasets & losses implemented in PyTorch

    Semantic segmentation models, datasets and losses implemented in PyTorch. PyTorch and Torchvision needs to be installed before running the scripts, together with PIL and opencv for data-preprocessing and tqdm for showing the training progress. PyTorch v1.1 is supported (using the new supported tensoboard); can work with earlier versions, but instead of using tensoboard, use tensoboardX. Poly learning rate, where the learning rate is scaled down linearly from the starting value down to zero...
    Downloads: 3 This Week
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  • 6
    Deepvoice3_pytorch

    Deepvoice3_pytorch

    PyTorch implementation of convolutional neural networks

    An open source implementation of Deep Voice 3: Scaling Text-to-Speech with Convolutional Sequence Learning.
    Downloads: 0 This Week
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  • 7
    3D ResNets for Action Recognition

    3D ResNets for Action Recognition

    3D ResNets for Action Recognition (CVPR 2018)

    We uploaded the pretrained models described in this paper including ResNet-50 pretrained on the combined dataset with Kinetics-700 and Moments in Time. We significantly updated our scripts. If you want to use older versions to reproduce our CVPR2018 paper, you should use the scripts in the CVPR2018 branch.
    Downloads: 0 This Week
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