Showing 7 open source projects for "deep"

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  • 1
    Sysdig

    Sysdig

    Linux system exploration and troubleshooting tool

    ...Unify threat detection and incident response across containers, Kubernetes, and cloud with out-of-the-box Falco rules leveraging syscalls, Kubernetes audit logs and cloud logs. Gain deep insight with container and Kubernetes monitoring that is fully Prometheus compatible. Validate compliance against standards like PCI, NIST and SOC2 for containers, hosts, Kubernetes and cloud. Sysdig created Falco, the open standard for runtime threat detection for containers, hosts, Kubernetes and cloud.
    Downloads: 1 This Week
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  • 2
    OpenFace Face Recognition

    OpenFace Face Recognition

    Face recognition with deep neural networks

    OpenFace is a Python and Torch implementation of face recognition with deep neural networks and is based on the CVPR 2015 paper FaceNet: A Unified Embedding for Face Recognition and Clustering by Florian Schroff, Dmitry Kalenichenko, and James Philbin at Google. Torch allows the network to be executed on a CPU or with CUDA. This research was supported by the National Science Foundation (NSF) under grant number CNS-1518865.
    Downloads: 2 This Week
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  • 3
    CycleGAN

    CycleGAN

    Software that can generate photos from paintings

    CycleGAN — in its original form — is a landmark in deep learning for image-to-image translation without paired data. Rather than requiring matching image pairs between source and target domains (which are often hard or impossible to obtain), CycleGAN learns two mappings — one from domain A to B, and another back from B to A — along with a cycle-consistency loss that encourages the round-trip to reconstruct the original image.
    Downloads: 0 This Week
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  • 4
    ResNeXt

    ResNeXt

    Implementation of a classification framework

    ResNeXt is a deep neural network architecture for image classification built on the idea of aggregated residual transformations. Instead of simply increasing depth or width, ResNeXt introduces a new dimension called cardinality, which refers to the number of parallel transformation paths (i.e. the number of “branches”) that are aggregated together.
    Downloads: 0 This Week
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  • 5
    DCGAN.torch

    DCGAN.torch

    Train your own image generator

    DCGAN.torch is an open-source implementation of Deep Convolutional Generative Adversarial Networks using the Torch7 machine learning framework. It allows developers to train image generators that create synthetic pictures from random latent vectors. The project follows the architecture introduced in the DCGAN research paper and includes configurable generator and discriminator networks.
    Downloads: 0 This Week
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  • 6
    waifu2x

    waifu2x

    Single-image super-resolution for anime-style art

    Single-Image Super-Resolution for Anime-Style Art using Deep Convolutional Neural Networks. And it supports photo. You can train your own model, change image size, reduce image noise, upscale and customize your image's style. It provides the option of converting and downloading your edited images.
    Downloads: 2 This Week
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  • 7
    fast-neural-style

    fast-neural-style

    Feedforward style transfer

    ...It also provides insights into the underlying techniques used in neural style transfer, making it both a practical tool and a learning resource. By combining performance and quality, it enables creative applications in image processing and design. Overall, fast-neural-style showcases how deep learning can be used for real-time artistic transformations.
    Downloads: 0 This Week
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