Search Results for "iris recognition source code python"
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2 projects for "iris recognition source code python" with 2 filters applied:
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FusionAuth: Authentication and User Management SoftwareFusionAuth adds login, registration, SSO, MFA, and a bazillion other features to your app in days - not months.
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ServiceDesk Plus, a world-class IT and enterprise service management platformBest in class online service desk software. Offer your customers world-class services with ServiceDesk Plus Cloud, the easy-to-use SaaS service desk software from ManageEngine, the IT management division of Zoho. Track and manage IT tickets efficiently, resolve issues faster, and ensure end-user satisfaction with the cloud-based IT ticketing system used by over 100,000 IT service desks worldwide. Manage the complete life cycle of IT incidents, problems, changes, and projects with out of the box ITIL workflows. Create support SLAs, define escalation levels, and ensure compliance. Automate ticket dispatch, categorization, classification, and assignment based on predefined business rules, and set up notifications and alerts for timely ticket resolution. Reduce walk ins and unnecessary tickets by giving your users more control. Enable end users to access IT services through your service catalog in the self-service portal. Help users create and track tickets and search for solutions.
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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,... -
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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...
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