Showing 3 open source projects for "highlight"

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    Advanced AI explainability for PyTorch

    Advanced AI explainability for PyTorch

    Advanced AI Explainability for computer vision

    pytorch-grad-cam is an open-source library that provides advanced explainable AI techniques for interpreting the predictions of deep learning models used in computer vision. The project implements Grad-CAM and several related visualization methods that highlight the regions of an image that most strongly influence a neural network’s decision. These visualization techniques allow developers and researchers to better understand how convolutional neural networks and transformer-based vision models make predictions. The library supports a wide variety of tasks including image classification, object detection, semantic segmentation, and similarity analysis. ...
    Downloads: 2 This Week
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  • 2
    ELI5

    ELI5

    A library for debugging/inspecting machine learning classifiers

    ...The library allows users to inspect model weights, analyze decision trees, and compute permutation feature importance for black-box models. It also provides specialized tools such as TextExplainer, which can highlight important words in text classification tasks to explain why a model produced a particular prediction. Additionally, the library integrates explanation algorithms such as LIME to interpret predictions from arbitrary machine learning models.
    Downloads: 0 This Week
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  • 3
    Intel neon

    Intel neon

    Intel® Nervana™ reference deep learning framework

    neon is Intel's reference deep learning framework committed to best performance on all hardware. Designed for ease of use and extensibility. See the new features in our latest release. We want to highlight that neon v2.0.0+ has been optimized for much better performance on CPUs by enabling Intel Math Kernel Library (MKL). The DNN (Deep Neural Networks) component of MKL that is used by neon is provided free of charge and downloaded automatically as part of the neon installation. The gpu backend is selected by default, so the above command is equivalent to if a compatible GPU resource is found on the system. ...
    Downloads: 0 This Week
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