2 projects for "iris recognition source code python" with 2 filters applied:

  • Planfix: Manage Projects, Team's Tasks and Business Processes Icon
    Planfix: Manage Projects, Team's Tasks and Business Processes

    All-in-One Enterprise-Level Software is Now Available for SMB

    Planfix is like a souped-up business process management system for folks who really know their stuff. It's built to help you dive deeper and gives you more options than your run-of-the-mill project and task management systems. Best part? Even small businesses and non-profits can get in on the action.
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  • Paladin Point of Sale is a powerful retail management system designed to simplify daily store operations for independent retailers. Icon
    Paladin Point of Sale is a powerful retail management system designed to simplify daily store operations for independent retailers.

    It enables businesses to sell from anywhere using mobile point-of-sale tools while also providing a ready-built online store for seamless omnichannel

    Paladin Point of Sale is ideal for independent retailers in hardware, lumber, pharmacy, and specialty retail industries seeking an easy-to-use, industry-specific POS system with strong support and flexibility.
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  • 1
    Open Model Zoo

    Open Model Zoo

    Pre-trained Deep Learning models and demos

    Open Model Zoo is a large repository of high-quality pre-trained deep learning models and demonstration applications designed to work with the OpenVINO™ toolkit, offering a comprehensive starting point for a wide range of AI and computer vision workloads. It includes hundreds of models covering object detection, classification, segmentation, pose estimation, speech recognition, text-to-speech, and more, many of which are already converted into formats optimized for inference on CPUs, GPUs,...
    Downloads: 0 This Week
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  • 2
    vJEPA-2

    vJEPA-2

    PyTorch code and models for VJEPA2 self-supervised learning from video

    VJEPA2 is a next-generation self-supervised learning framework for video that extends the “predict in representation space” idea from i-JEPA to the temporal domain. Instead of reconstructing pixels, it predicts the missing high-level embeddings of masked space-time regions using a context encoder and a slowly updated target encoder. This objective encourages the model to learn semantics, motion, and long-range structure without the shortcuts that pixel-level losses can invite. The...
    Downloads: 0 This Week
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