Update README.md

README.md

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 short_description: Compare Edge AI model performance - Latency and accuracy ben
 ---
+tags:
+  - edge-ai
+  - aiot
+  - model-benchmark
+  - gradio
+  - iot
+  - ml-performance
+  - edge-computing
+---
+
+# Edge AI Model Benchmark for IoT Deployment — Anktechsol
+
+**Anktechsol** specializes in Edge AI and AIoT solutions, helping organizations select and deploy optimal AI models on resource-constrained IoT devices. This benchmark tool provides real-world performance metrics for popular Edge AI models, enabling data-driven decisions for model selection in industrial IoT, smart cities, and embedded AI applications.
+
+## 🚀 Why Edge AI Benchmarking Matters
+
+Deploying AI models on edge devices requires balancing multiple factors: inference latency, accuracy, model size, and power consumption. This tool helps you compare models like MobileNetV2, EfficientNet, SqueezeNet, and others across these critical dimensions.
+
+## 📊 Key Features
+
+- **Multi-Model Comparison**: Compare 6+ popular edge AI architectures
+- **Real Performance Metrics**: Latency (ms), Accuracy (%), Model Size (MB), Power (mW)
+- **Interactive Visualizations**: Side-by-side bar charts and comprehensive dashboards
+- **IoT-Optimized**: Metrics based on typical edge hardware (ARM Cortex, mobile SoCs)
+- **Instant Insights**: Identify the best model for your latency/accuracy requirements
+- **No Setup Required**: Browser-based tool with immediate access
+
+## 💡 How to Use
+
+1. **Single Metric Mode**: Select two models and one metric for focused comparison
+2. **Complete Benchmark Mode**: View all 4 metrics simultaneously for comprehensive analysis
+3. **Interpret Results**: Lower latency and power = better; Higher accuracy = better
+
+## 🏭 Use Cases
+
+- **Edge AI Deployment Planning**: Choose the right model before hardware investment
+- **IoT Product Development**: Balance performance vs. resource constraints
+- **Industrial Automation**: Select models for real-time decision-making
+- **Smart Device Design**: Optimize for battery life and response time
+- **Research & Development**: Compare baseline performance for new architectures
+- **Technical Sales**: Demonstrate model capabilities to clients
+
+## 👥 Who Should Use This
+
+- **ML Engineers** deploying models to edge devices
+- **IoT Architects** designing AIoT systems
+- **Product Managers** evaluating Edge AI capabilities
+- **Embedded Systems Developers** optimizing for constrained hardware
+- **Data Scientists** selecting models for production deployment
+- **Students & Researchers** learning about Edge AI performance trade-offs
+
+## 📝 Models Included
+
+- **MobileNetV2**: Efficient mobile-first architecture
+- **EfficientNet-B0**: High accuracy with reasonable latency
+- **SqueezeNet**: Ultra-compact model for extreme edge
+- **ResNet18**: Proven architecture for computer vision
+- **TinyYOLO**: Lightweight object detection
+- **MobileViT-S**: Vision transformer for mobile
+
+## 🔗 Backlinks & Resources
+
+**Anktechsol** - Your AIoT Implementation Partner:
+- [Visit Anktechsol](https://anktechsol.com) for Edge AI consulting
+- [Explore AIoT Tools](https://huggingface.co/anktechsol) on Hugging Face
+- Contact us for custom model optimization and deployment services
+
+## 🏷️ Keywords
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+Edge AI, AIoT, model benchmarking, edge computing, IoT model selection, ML performance, inference latency, edge ML optimization, mobile AI, embedded AI, TinyML, Edge AI comparison, IoT analytics, industrial AI, smart devices, on-device AI, Edge AI deployment, model compression, neural network efficiencyck out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference