🚀 Update: AnomaVision: Deploy ONNX + OpenCV in C++ (vs Python) ⚡ Real-Time Anomaly Detection
👋 Welcome to this end-to-end AI deployment tutorial where we take AnomaVision (an anomaly detection project) from Python prototyping to production-ready C++ using ONNX Runtime + OpenCV.
Github: https://github.com/DeepKnowledge1/Ano...
Whether you’re a beginner 👨💻 or a pro developer 💼, this video is packed with insights to help you understand how to:
✅ Export a trained model to ONNX
✅ Run real-time inference with OpenCV + ONNX Runtime in C++
✅ Compare C++ vs Python performance (fps, latency, stability)
✅ Visualize anomaly heatmaps & results with overlays
✅ Benchmark inference speed like a professional
✅ Apply best practices for deployment in production environments
⚡ Performance Comparison:
🔥 C++ (ONNX + OpenCV): ~38.9 ms avg → ~25.7 FPS
🔥 Python (ONNX Runtime): Slower, ~28–35 FPS depending on setup
➡️ Result: C++ gives more stable real-time inference ⚡
🤖 Project Features:
Input: 224×224 RGB images
Output 1: Anomaly score (numeric)
Output 2: Heatmap + anomaly mask
Real-world use case: manufacturing defect detection 🏭
💼 Who Is This For?
🔰 Beginners → Learn step-by-step AI deployment
🔥 Professionals → Advanced optimization tips for C++ & ONNX
🎓 Students → Build a portfolio project that stands out
💼 Job Seekers → Master interview-ready deployment skills
🛠 Tech Stack Used:
ONNX Runtime (C++ API)
OpenCV (image preprocessing & visualization)
CMake (cross-platform build system)
High-precision timing & profiling tools
🎨 Why Watch This?
✨ Not a toy demo — it’s a real project
✨ Full Python-to-C++ workflow explained
✨ Performance analysis with real inference results
✨ Beginner-friendly yet pro-level insights
💬 Engage With Me!
👍 Smash the like button if this helps your AI journey
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🔖 Hashtags
#onnx #cpp #opencv #anomavision #ai #machinelearning #computervision #deeplearning #aideployment #productionai #mlops #programming #tutorial #pythonvspcpp #neuralnetworks #anomalydetection