Showing 11 open source projects for "pkg-config"

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  • 1
    GoCV

    GoCV

    Go package for computer vision using OpenCV 4 and beyond

    GoCV gives programmers who use the Go programming language access to the OpenCV 4 computer vision library. The GoCV package supports the latest releases of Go and OpenCV v4.5.4 on Linux, macOS, and Windows. Our mission is to make the Go language a “first-class” client compatible with the latest developments in the OpenCV ecosystem. Computer Vision (CV) is the ability of computers to process visual information, and perform tasks normally associated with those performed by humans. CV software...
    Downloads: 3 This Week
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  • 2
    Arize Phoenix

    Arize Phoenix

    Uncover insights, surface problems, monitor, and fine tune your LLM

    Phoenix provides ML insights at lightning speed with zero-config observability for model drift, performance, and data quality. Phoenix is an Open Source ML Observability library designed for the Notebook. The toolset is designed to ingest model inference data for LLMs, CV, NLP and tabular datasets. It allows Data Scientists to quickly visualize their model data, monitor performance, track down issues & insights, and easily export to improve.
    Downloads: 7 This Week
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  • 3
    Thinc

    Thinc

    A refreshing functional take on deep learning

    Thinc is a lightweight deep learning library that offers an elegant, type-checked, functional-programming API for composing models, with support for layers defined in other frameworks such as PyTorch, TensorFlow and MXNet. You can use Thinc as an interface layer, a standalone toolkit or a flexible way to develop new models. Previous versions of Thinc have been running quietly in production in thousands of companies, via both spaCy and Prodigy. We wrote the new version to let users compose,...
    Downloads: 3 This Week
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  • 4
    SSD in PyTorch 1.0

    SSD in PyTorch 1.0

    High quality, fast, modular reference implementation of SSD in PyTorch

    ...You can replace every component with your own code without changing the code base. For example, You can add EfficientNet as the backbone, just add efficient_net.py (ALREADY ADDED) and register it, specific it in the config file, It's done! Smooth and enjoyable training procedure: we save the state of model, optimizer, scheduler, training iter, you can stop your training and resume training exactly from the save point without change your training CMD.
    Downloads: 0 This Week
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  • 5
    Lightning-Hydra-Template

    Lightning-Hydra-Template

    PyTorch Lightning + Hydra. A very user-friendly template

    ...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.
    Downloads: 0 This Week
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  • 6
    Exadel CompreFace

    Exadel CompreFace

    Leading free and open-source face recognition system

    ...The solution also features a role management system that allows you to easily control who has access to your Face Recognition Services. CompreFace is delivered as a docker-compose config and supports different models that work on CPU and GPU. Our solution is based on state-of-the-art methods and libraries like FaceNet and InsightFace. Official website: https://exadel.com/solutions/compreface/ Github link: https://github.com/exadel-inc/CompreFace
    Downloads: 7 This Week
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  • 7
    ModelFox

    ModelFox

    ModelFox makes it easy to train, deploy, and monitor ML models

    ...The CLI automatically transforms your data into features, trains a number of linear and gradient boosted decision tree models to predict the target column, and writes the best model to a .modelfox file. If you want more control, you can provide a config file.
    Downloads: 0 This Week
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  • 8
    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: 0 This Week
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  • 9
    ModelDB

    ModelDB

    Open Source ML Model Versioning, Metadata, and Experiment Management

    An open-source system for Machine Learning model versioning, metadata, and experiment management. ModelDB is an open-source system to version machine learning models including their ingredients code, data, config, and environment and to track ML metadata across the model lifecycle.
    Downloads: 0 This Week
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  • 10
    Oryx

    Oryx

    Lambda architecture on Apache Spark, Apache Kafka for real-time

    ...It is a framework for building applications but also includes packaged, end-to-end applications for collaborative filtering, classification, regression and clustering. The application is written in Java, using Apache Spark, Hadoop, Tomcat, Kafka, Zookeeper and more. Configuration uses a single Typesafe Config config file, wherein applications configure an entire deployment of the system. This includes implementations of key interface classes which implement the batch, speed, and serving logic. Applications package and deploy their implementations with each instance of the layer binaries. Each of these is a runnable Java .jar which starts all necessary services.
    Downloads: 0 This Week
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  • 11
    DeepLearningProject

    DeepLearningProject

    An in-depth machine learning tutorial

    This tutorial tries to do what most Most Machine Learning tutorials available online do not. It is not a 30 minute tutorial that teaches you how to "Train your own neural network" or "Learn deep learning in under 30 minutes". It's a full pipeline which you would need to do if you actually work with machine learning - introducing you to all the parts, and all the implementation decisions and details that need to be made. The dataset is not one of the standard sets like MNIST or CIFAR, you...
    Downloads: 0 This Week
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