What is VISNET Framework?
VISNET Framework is a state-of-the-art platform designed to facilitate the development and deployment of artificial intelligence models, specializing in natural language processing (NLP) and Deep Vision systems. It offers a headless, multi-compatible interface crafted to serve as a comprehensive solution for researchers, developers, and industries. The framework includes a modular frontend, a high-performance gateway for seamless connectivity, robust authentication protocols, and core AI models for translation, license plate recognition, and face feature matching.
The solution integrates powerful features such as DDOS protection, IP filtering, Oauth 2.0 for security, RSA encryption, and offers turnkey solutions in surveillance and autonomous inspections through advanced image and video analysis capabilities. From facial recognition to live audio transcription and vehicle detection at exceptional speeds, Visnet Framework is built to tackle highly complex problems with precision and efficiency.
Features
- High-Performance Gateway: Universal ASGI, DDOS protection, IP filtering.
- Auth Protocol Layer: Robust security with Oauth 2.0, RSA encryption, and access control.
- Core AI Models: Advanced capabilities including translation, face recognition, and vehicle number detection.
Use Cases
- Surveillance security systems
- Autonomous drone inspections
- Image and video analysis
- Real-time audio transcription
- Vehicle detection and identification
FAQs
-
What are the key features of VISNET Framework?
VISNET Framework offers a high-performance gateway, robust security with Oauth 2.0 and RSA encryption, and advanced AI models for translation, face recognition, and vehicle number detection. -
What use-cases does VISNET Framework cover?
VISNET Framework is ideal for surveillance security systems, autonomous drone inspections, image and video analysis, real-time audio transcription, and vehicle detection and identification.
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VISNET Framework Uptime Monitor
Average Uptime
99.67%
Average Response Time
113.5 ms
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