bevfusion
fcf3ae0ea9
🎯 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 |
||
|---|---|---|
| .. | ||
| CMakeLists.txt | ||
| README.md | ||
| main.cc | ||
README.md
YOLOv8 LibTorch Inference C++
This example demonstrates how to perform inference using YOLOv8 models in C++ with LibTorch API.
Dependencies
| Dependency | Version |
|---|---|
| OpenCV | >=4.0.0 |
| C++ Standard | >=17 |
| Cmake | >=3.18 |
| Libtorch | >=1.12.1 |
Usage
git clone ultralytics
cd ultralytics
pip install .
cd examples/YOLOv8-LibTorch-CPP-Inference
mkdir build
cd build
cmake ..
make
./yolov8_libtorch_inference
Exporting YOLOv8
To export YOLOv8 models:
yolo export model=yolov8s.pt imgsz=640 format=torchscript