Spaces:
Running
Running
| title: README | |
| emoji: 📚 | |
| colorFrom: red | |
| colorTo: green | |
| sdk: static | |
| pinned: true | |
| # Vision Perception & Interpretation | |
| Welcome to **Perception365** | |
| We work at the intersection of computer vision, real-world perception, and edge AI. | |
| > Vision intelligence should be accessible — even at the smallest scale. | |
| Even highly specialized models often fail to exhibit their true performance once deployed outside curated datasets. Ignoring this gap leads to unreliable systems, poor generalization, and real-world failure. | |
| We focus on closing this gap. | |
| Our work prioritizes the accuracy–latency Pareto frontier, ensuring models are both practical and performant. | |
| --- | |
| This page hosts a collection of vision models optimized for edge deployment or GPU inference, including: | |
| Each model is designed with: | |
| - Low compute and memory footprints | |
| - Real-world robustness | |
| - Deployment readiness on edge devices | |
| Perception is a long road. | |
| We’re not claiming perfection — just progress. | |
| If you care about real-world vision systems, edge AI, and practical deployment, you’re in the right place. | |
| Built for the real world. Designed for the edge. | |