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
Models
Welcome to the Ultralytics Models directory! Here you will find a wide variety of pre-configured model configuration files (*.yamls) that can be used to create custom YOLO models. The models in this directory have been expertly crafted and fine-tuned by the Ultralytics team to provide the best performance for a wide range of object detection and image segmentation tasks.
These model configurations cover a wide range of scenarios, from simple object detection to more complex tasks like instance segmentation and object tracking. They are also designed to run efficiently on a variety of hardware platforms, from CPUs to GPUs. Whether you are a seasoned machine learning practitioner or just getting started with YOLO, this directory provides a great starting point for your custom model development needs.
To get started, simply browse through the models in this directory and find one that best suits your needs. Once you've selected a model, you can use the provided *.yaml file to train and deploy your custom YOLO model with ease. See full details at the Ultralytics Docs, and if you need help or have any questions, feel free to reach out to the Ultralytics team for support. So, don't wait, start creating your custom YOLO model now!
Usage
Model *.yaml files may be used directly in the Command Line Interface (CLI) with a yolo command:
# Train a YOLOv8n model using the coco8 dataset for 100 epochs
yolo task=detect mode=train model=yolov8n.yaml data=coco8.yaml epochs=100
They may also be used directly in a Python environment, and accept the same arguments as in the CLI example above:
from ultralytics import YOLO
# Initialize a YOLOv8n model from a YAML configuration file
model = YOLO("model.yaml")
# If a pre-trained model is available, use it instead
# model = YOLO("model.pt")
# Display model information
model.info()
# Train the model using the COCO8 dataset for 100 epochs
model.train(data="coco8.yaml", epochs=100)
Pre-trained Model Architectures
Ultralytics supports many model architectures. Visit Ultralytics Models to view detailed information and usage. Any of these models can be used by loading their configurations or pretrained checkpoints if available.
Contribute New Models
Have you trained a new YOLO variant or achieved state-of-the-art performance with specific tuning? We'd love to showcase your work in our Models section! Contributions from the community in the form of new models, architectures, or optimizations are highly valued and can significantly enrich our repository.
By contributing to this section, you're helping us offer a wider array of model choices and configurations to the community. It's a fantastic way to share your knowledge and expertise while making the Ultralytics YOLO ecosystem even more versatile.
To get started, please consult our Contributing Guide for step-by-step instructions on how to submit a Pull Request (PR) 🛠️. Your contributions are eagerly awaited!
Let's join hands to extend the range and capabilities of the Ultralytics YOLO models 🙏!