Showing 4 open source projects for "train ai"

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  • 1
    Habitat-Sim

    Habitat-Sim

    A flexible, high-performance 3D simulator for Embodied AI research

    ...Determinism and reproducibility are first-class goals, which is critical for benchmarking agents and comparing algorithms. Thanks to its speed and modular design, Habitat-Sim is widely used to prototype embodied agents, train at scale, and evaluate in standardized environments with consistent metrics.
    Downloads: 7 This Week
    Last Update:
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  • 2
    Kornia

    Kornia

    Open Source Differentiable Computer Vision Library

    ...At its core, the package uses PyTorch as its main backend both for efficiency and to take advantage of the reverse-mode auto-differentiation to define and compute the gradient of complex functions. Inspired by existing packages, this library is composed by a subset of packages containing operators that can be inserted within neural networks to train models to perform image transformations, epipolar geometry, depth estimation, and low-level image processing such as filtering and edge detection that operate directly on tensors. With Kornia we fill the gap between classical and deep computer vision that implements standard and advanced vision algorithms for AI. Our libraries and initiatives are always according to the community needs.
    Downloads: 8 This Week
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  • 3
    DALL-E 2 - Pytorch

    DALL-E 2 - Pytorch

    Implementation of DALL-E 2, OpenAI's updated text-to-image synthesis

    Implementation of DALL-E 2, OpenAI's updated text-to-image synthesis neural network, in Pytorch. The main novelty seems to be an extra layer of indirection with the prior network (whether it is an autoregressive transformer or a diffusion network), which predicts an image embedding based on the text embedding from CLIP. Specifically, this repository will only build out the diffusion prior network, as it is the best performing variant (but which incidentally involves a causal transformer as...
    Downloads: 8 This Week
    Last Update:
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  • 4
    Image Super-Resolution (ISR)

    Image Super-Resolution (ISR)

    Super-scale your images and run experiments with Residual Dense

    The goal of this project is to upscale and improve the quality of low-resolution images. This project contains Keras implementations of different Residual Dense Networks for Single Image Super-Resolution (ISR) as well as scripts to train these networks using content and adversarial loss components. Docker scripts and Google Colab notebooks are available to carry training and prediction. Also, we provide scripts to facilitate training on the cloud with AWS and Nvidia-docker with only a few...
    Downloads: 3 This Week
    Last Update:
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