Showing 4 open source projects for "rf"

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  • 1
    RF-DETR

    RF-DETR

    RF-DETR is a real-time object detection and segmentation

    RF-DETR is an open-source computer vision framework that implements a real-time object detection and instance segmentation model based on transformer architectures. Developed by Roboflow, the project builds upon modern vision transformer backbones such as DINOv2 to achieve strong accuracy while maintaining efficient inference speeds suitable for real-time applications.
    Downloads: 5 This Week
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  • 2
    FISSURE

    FISSURE

    The RF and reverse engineering framework for everyone

    FISSURE is an open-source radio frequency analysis and signal intelligence framework built to support software-defined radio research, wireless security experimentation, and protocol reverse engineering. The project brings together tools for capturing, inspecting, decoding, replaying, and analyzing RF signals across a wide range of wireless technologies. It is designed as a practical environment for researchers and operators who need to move from raw spectrum observation to structured investigation without stitching together too many separate utilities by hand. The platform supports workflows related to signal discovery, demodulation, packet inspection, fuzzing, and attack simulation, making it useful for both defensive research and controlled lab testing. ...
    Downloads: 2 This Week
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  • 3
    AutoMLPipeline.jl

    AutoMLPipeline.jl

    Package that makes it trivial to create and evaluate machine learning

    ...To illustrate, here is a pipeline expression and evaluation of a typical machine learning workflow that extracts numerical features (numf) for ica (Independent Component Analysis) and pca (Principal Component Analysis) transformations, respectively, concatenated with the hot-bit encoding (ohe) of categorical features (catf) of a given data for rf (Random Forest) modeling.
    Downloads: 0 This Week
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  • 4

    Unsupervised Random Forest

    On-line Unsupervised Random Forest

    ...For the sake of efficiency and meeting the dynamism requirement of autonomic clouds, our methodology consists of two steps: (i) off-line clustering and (ii) on-line prediction. RF+PAM can: Cluster observations (Unsupervised Learning) Calculate the dissimilarity between 2 or more observations (how different two observations are)
    Downloads: 0 This Week
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