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Showing 2 open source projects for "murgee activation code"

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  • The Most Awarded Employee Time Clock Software Icon
    The Most Awarded Employee Time Clock Software

    For businesses who have employees they need to track time, attendance, or schedule.

    Cloud based time clock solution that pre-populates reports for payroll. Employees can punch in on their desktop or mobile devices. Punching in & out is intuitive for your employees & easy for you to view & export time. Employees can clock in using a browser or our Google, iOS, & Android apps. You can view who's working, their GPS position or even limit where they can punch. We integrate with QuickBooks, ADP, Paychex, & SurePayroll while also offering Excel exports. Advanced features such as PTO Accrual Tracking, Punch Rounding, Job Codes, QR Codes, Automatic Breaks, & SSO are all included in our cloud based time clock.
    Learn More
  • Kinetic Software - Epicor ERP Icon
    Kinetic Software - Epicor ERP

    Discrete, make-to-order and mixed-mode manufacturers who need a global cloud ERP solution

    Grow, thrive, and compete in a global marketplace with Kinetic—an industry-tailored, cognitive ERP that helps you work smarter and stay connected.
    Learn More
  • 1
    ConvNeXt

    ConvNeXt

    Code release for ConvNeXt model

    ConvNeXt is a modernized convolutional neural network (CNN) architecture designed to rival Vision Transformers (ViTs) in accuracy and scalability while retaining the simplicity and efficiency of CNNs. It revisits classic ResNet-style backbones through the lens of transformer design trends—large kernel sizes, inverted bottlenecks, layer normalization, and GELU activations—to bridge the performance gap between convolutions and attention-based models. ConvNeXt’s clean, hierarchical structure...
    Downloads: 1 This Week
    Last Update:
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  • 2
    CAM

    CAM

    Class Activation Mapping

    This repository implements Class Activation Mapping (CAM), a technique to expose the implicit attention of convolutional neural networks by generating heatmaps that highlight the most discriminative image regions influencing a network’s class prediction. The method involves modifying a CNN model slightly (e.g., using global average pooling before the final layer) to produce a weighted combination of feature maps as the class activation map.
    Downloads: 0 This Week
    Last Update:
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