Скачать или смотреть Real-Time Object Detection: GPU vs. CPU (YOLOv11n OpenVINO INT8)

Is a powerful GPU always necessary for real-time AI? This video puts that idea to the test!

Watch this side-by-side comparison of a YOLOv11n object detection model running on the same video feed.

➡️ *On the LEFT:* The baseline model running on a powerful NVIDIA GPU (RTX 2080 Ti).
➡️ *On the RIGHT:* The same model after being heavily optimized with OpenVINO and INT8 quantization, running on a standard Intel CPU!

The goal is to see if we can achieve real-time, high-frame-rate performance for Edge AI applications without needing expensive, power-hungry GPUs. This is crucial for deploying deep learning models on resource-constrained devices like Raspberry Pi, Jetson boards, and other embedded systems.

The results are pretty amazing! This demonstrates the power of model optimization techniques like quantization in bridging the gap between powerful AI models and practical, real-world edge deployment.

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LEARN MORE IN OUR DETAILED BLOG SERIES:
Want to know exactly how we did this? Check out our full, step-by-step blog series on optimizing AI models for the edge:
🔗 https://isahil.me/blogs/edge_ai_showd...

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// Tech Specs & Keywords for Search

This project uses a YOLOv11n (nano) model for a tomato ripeness classification task. The key technologies demonstrated are:

AI Model: YOLOv11n
Optimization Toolkit: Intel OpenVINO
Technique: Post-Training Quantization (PTQ), INT8 Precision
Use Case: Edge AI, Computer Vision, Object Detection, Smart Agriculture, Robotics
Hardware Comparison: GPU (NVIDIA) vs. CPU (Intel)
Goal: Real-time inference, low latency, high FPS for edge devices.

#EdgeAI #OpenVINO #YOLO #ComputerVision #DeepLearning #Optimization #INT8 #RaspberryPi #JetsonNano