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 |
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| .. | ||
| YOLOv8-Action-Recognition | ||
| YOLOv8-CPP-Inference | ||
| YOLOv8-LibTorch-CPP-Inference | ||
| YOLOv8-ONNXRuntime | ||
| YOLOv8-ONNXRuntime-CPP | ||
| YOLOv8-ONNXRuntime-Rust | ||
| YOLOv8-OpenCV-ONNX-Python | ||
| YOLOv8-OpenCV-int8-tflite-Python | ||
| YOLOv8-OpenVINO-CPP-Inference | ||
| YOLOv8-Region-Counter | ||
| YOLOv8-SAHI-Inference-Video | ||
| YOLOv8-Segmentation-ONNXRuntime-Python | ||
| README.md | ||
| heatmaps.ipynb | ||
| hub.ipynb | ||
| object_counting.ipynb | ||
| object_tracking.ipynb | ||
| tutorial.ipynb | ||
README.md
Ultralytics YOLOv8 Example Applications
This repository features a collection of real-world applications and walkthroughs, provided as either Python files or notebooks. Explore the examples below to see how YOLOv8 can be integrated into various applications.
Ultralytics YOLO Example Applications
| Title | Format | Contributor |
|---|---|---|
| YOLO ONNX Detection Inference with C++ | C++/ONNX | Justas Bartnykas |
| YOLO OpenCV ONNX Detection Python | OpenCV/Python/ONNX | Farid Inawan |
| YOLOv8 .NET ONNX ImageSharp | C#/ONNX/ImageSharp | Compunet |
| YOLO .Net ONNX Detection C# | C# .Net | Samuel Stainback |
| YOLOv8 on NVIDIA Jetson(TensorRT and DeepStream) | Python | Lakshantha |
| YOLOv8 ONNXRuntime Python | Python/ONNXRuntime | Semih Demirel |
| YOLOv8 ONNXRuntime CPP | C++/ONNXRuntime | DennisJcy, Onuralp Sezer |
| RTDETR ONNXRuntime C# | C#/ONNX | Kayzwer |
| YOLOv8 SAHI Video Inference | Python | Muhammad Rizwan Munawar |
| YOLOv8 Region Counter | Python | Muhammad Rizwan Munawar |
| YOLOv8 Segmentation ONNXRuntime Python | Python/ONNXRuntime | jamjamjon |
| YOLOv8 LibTorch CPP | C++/LibTorch | Myyura |
| YOLOv8 OpenCV INT8 TFLite Python | Python | Wamiq Raza |
| YOLOv8 All Tasks ONNXRuntime Rust | Rust/ONNXRuntime | jamjamjon |
How to Contribute
We greatly appreciate contributions from the community, including examples, applications, and guides. If you'd like to contribute, please follow these guidelines:
- Create a pull request (PR) with the title prefix
[Example], adding your new example folder to theexamples/directory within the repository. - Ensure your project adheres to the following standards:
- Makes use of the
ultralyticspackage. - Includes a
README.mdwith clear instructions for setting up and running the example. - Avoids adding large files or dependencies unless they are absolutely necessary for the example.
- Contributors should be willing to provide support for their examples and address related issues.
- Makes use of the
For more detailed information and guidance on contributing, please visit our contribution documentation.
If you encounter any questions or concerns regarding these guidelines, feel free to open a PR or an issue in the repository, and we will assist you in the contribution process.