History

bevfusion fcf3ae0ea9 Complete project state snapshot: Phase 4B RMT-PPAD Integration 🎯 Training Status: - Current Epoch: 2/10 (13.3% complete) - Segmentation Dice: 0.9594 - Detection IoU: 0.5742 - Training stable with 8 GPUs 🔧 Technical Achievements: - ✅ RMT-PPAD Transformer segmentation decoder integrated - ✅ Task-specific GCA architecture optimized - ✅ Multi-scale feature fusion (180×180, 360×360, 600×600) - ✅ Adaptive scale weight learning implemented - ✅ BEVFusion multi-task framework enhanced 📊 Performance Highlights: - Divider segmentation: 0.9793 Dice (excellent) - Pedestrian crossing: 0.9812 Dice (excellent) - Stop line: 0.9812 Dice (excellent) - Carpark area: 0.9802 Dice (excellent) - Walkway: 0.9401 Dice (good) - Drivable area: 0.8959 Dice (good) 🛠️ Code Changes Included: - Enhanced BEVFusion model (bevfusion.py) - RMT-PPAD integration modules (rmtppad_integration.py) - Transformer segmentation head (enhanced_transformer.py) - GCA module optimizations (gca.py) - Configuration updates (Phase 4B configs) - Training scripts and automation tools - Comprehensive documentation and analysis reports 📅 Snapshot Date: Fri Nov 14 09:06:09 UTC 2025 📍 Environment: Docker container 🎯 Phase: RMT-PPAD Integration Complete		2025-11-14 09:06:09 +00:00
..
CMakeLists.txt	Complete project state snapshot: Phase 4B RMT-PPAD Integration	2025-11-14 09:06:09 +00:00
README.md	Complete project state snapshot: Phase 4B RMT-PPAD Integration	2025-11-14 09:06:09 +00:00
inference.cc	Complete project state snapshot: Phase 4B RMT-PPAD Integration	2025-11-14 09:06:09 +00:00
inference.h	Complete project state snapshot: Phase 4B RMT-PPAD Integration	2025-11-14 09:06:09 +00:00
main.cc	Complete project state snapshot: Phase 4B RMT-PPAD Integration	2025-11-14 09:06:09 +00:00

README.md

YOLOv8 OpenVINO Inference in C++ 🦾

Welcome to the YOLOv8 OpenVINO Inference example in C++! This guide will help you get started with leveraging the powerful YOLOv8 models using OpenVINO and OpenCV API in your C++ projects. Whether you're looking to enhance performance or add flexibility to your applications, this example has got you covered.

🌟 Features

🚀 Model Format Support: Compatible with ONNX and OpenVINO IR formats.
⚡ Precision Options: Run models in FP32, FP16, and INT8 precisions.
🔄 Dynamic Shape Loading: Easily handle models with dynamic input shapes.

📋 Dependencies

To ensure smooth execution, please make sure you have the following dependencies installed:

Dependency	Version
OpenVINO	>=2023.3
OpenCV	>=4.5.0
C++	>=14
CMake	>=3.12.0

⚙️ Build Instructions

Follow these steps to build the project:

Clone the repository:

git clone https://github.com/ultralytics/ultralytics.git
cd ultralytics/YOLOv8-OpenVINO-CPP-Inference

Create a build directory and compile the project:
```
mkdir build
cd build
cmake ..
make
```

🛠️ Usage

Once built, you can run inference on an image using the following command:

./detect <model_path.{onnx, xml}> <image_path.jpg>

🔄 Exporting YOLOv8 Models

To use your YOLOv8 model with OpenVINO, you need to export it first. Use the command below to export the model:

yolo export model=yolov8s.pt imgsz=640 format=openvino

📸 Screenshots

Running Using OpenVINO Model

Running Using ONNX Model

❤️ Contributions

We hope this example helps you integrate YOLOv8 with OpenVINO and OpenCV into your C++ projects effortlessly. Happy coding! 🚀