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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README.md
YOLOv8 - Int8-TFLite Runtime
Welcome to the YOLOv8 Int8 TFLite Runtime for efficient and optimized object detection project. This README provides comprehensive instructions for installing and using our YOLOv8 implementation.
Installation
Ensure a smooth setup by following these steps to install necessary dependencies.
Installing Required Dependencies
Install all required dependencies with this simple command:
pip install -r requirements.txt
Installing tflite-runtime
To load TFLite models, install the tflite-runtime package using:
pip install tflite-runtime
Installing tensorflow-gpu (For NVIDIA GPU Users)
Leverage GPU acceleration with NVIDIA GPUs by installing tensorflow-gpu:
pip install tensorflow-gpu
Note: Ensure you have compatible GPU drivers installed on your system.
Installing tensorflow (CPU Version)
For CPU usage or non-NVIDIA GPUs, install TensorFlow with:
pip install tensorflow
Usage
Follow these instructions to run YOLOv8 after successful installation.
Convert the YOLOv8 model to Int8 TFLite format:
yolo export model=yolov8n.pt imgsz=640 format=tflite int8
Locate the Int8 TFLite model in yolov8n_saved_model. Choose best_full_integer_quant or verify quantization at Netron. Then, execute the following in your terminal:
python main.py --model yolov8n_full_integer_quant.tflite --img image.jpg --conf-thres 0.5 --iou-thres 0.5
Replace best_full_integer_quant.tflite with your model file's path, image.jpg with your input image, and adjust the confidence (conf-thres) and IoU thresholds (iou-thres) as necessary.
Output
The output is displayed as annotated images, showcasing the model's detection capabilities:
