Showing 3 open source projects for "environment-modules"

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    SDGym

    SDGym

    Benchmarking synthetic data generation methods

    ...In addition to performance and memory usage, you can also measure synthetic data quality and privacy through a variety of metrics. Install SDGym using pip or conda. We recommend using a virtual environment to avoid conflicts with other software on your device.
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  • 2
    MMGeneration

    MMGeneration

    MMGeneration is a powerful toolkit for generative models

    ...It's time to play with your GANs! For the highly dynamic training in generative models, we adopt a new way to train dynamic models with MMDDP. A new design for complex loss modules is proposed for customizing the links between modules, which can achieve flexible combinations among different modules. Conditional GANs have been supported in our toolkit. More methods and pre-trained weights will come soon.
    Downloads: 0 This Week
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  • 3
    StudioGAN

    StudioGAN

    StudioGAN is a Pytorch library providing implementations of networks

    ...The benchmark includes results from GANs (BigGAN-Deep, StyleGAN-XL), auto-regressive models (MaskGIT, RQ-Transformer), and Diffusion models (LSGM++, CLD-SGM, ADM-G-U). StudioGAN is a self-contained library that provides 7 GAN architectures, 9 conditioning methods, 4 adversarial losses, 13 regularization modules, 6 augmentation modules, 8 evaluation metrics, and 5 evaluation backbones. Among these configurations, we formulate 30 GANs as representatives. Each modularized option is managed through a configuration system that works through a YAML file.
    Downloads: 0 This Week
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