license: mit
language:
- en
library_name: pytorch
tags:
- computer-vision
- autonomous-driving
- self-driving
- interfuser
- carla
- object-detection
- trajectory-prediction
datasets:
- PDM-Lite-CARLA
pipeline_tag: object-detection
🚗 InterFuser-Baseer-v1: Autonomous Driving Model
🎮 Try the Live Demo - Experience the model in action with real-time autonomous driving simulation!
📖 Overview
InterFuser-Baseer-v1 is a state-of-the-art transformer-based model for autonomous driving, specifically fine-tuned for the Baseer Self-Driving API. This model combines computer vision and deep learning to provide real-time traffic object detection and trajectory planning in simulated driving environments.
🎯 Key Capabilities
- Multi-Task Learning: Simultaneous traffic object detection and waypoint prediction
- Transformer Architecture: Advanced attention mechanisms for scene understanding
- Real-Time Processing: Optimized for real-time inference in driving scenarios
- CARLA Integration: Specifically tuned for CARLA simulation environment
🏗️ Architecture
Model Components
| Component | Specification |
|---|---|
| Image Backbone | ResNet-50 (ImageNet pretrained) |
| LiDAR Backbone | ResNet-18 (disabled in this version) |
| Transformer | 6-layer encoder/decoder, 8 attention heads |
| Embedding Dimension | 256 |
| Prediction Heads | GRU-based waypoint predictor + Detection head |
Output Format
- Traffic Detection: 20×20×7 grid (confidence, position, dimensions, orientation)
- Waypoint Prediction: 10 future trajectory points
- Scene Understanding: Junction, traffic light, and stop sign detection
🚀 Quick Start
Installation
pip install torch torchvision timm huggingface_hub
Usage Example
import torch
from huggingface_hub import hf_hub_download
# Download model weights
model_path = hf_hub_download(
repo_id="BaseerAI/Interfuser-Baseer-v1",
filename="best_model.pth"
)
# Load model (requires InterFuser class definition)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = torch.load(model_path, map_location=device)
model.eval()
# Inference
with torch.no_grad():
outputs = model(input_data)
📊 Performance
Training Details
- Dataset: PDM-Lite-CARLA (Urban driving scenarios)
- Training Objective: Multi-task learning with IoU optimization
- Framework: PyTorch
Key Metrics
- Optimized for traffic detection accuracy
- Enhanced bounding box IoU performance
- Robust waypoint prediction in urban scenarios
⚠️ Limitations
Current Constraints
- Simulation Only: Trained exclusively on CARLA data
- Single Camera: Front-facing camera view only
- No LiDAR: Vision-based approach without LiDAR fusion
- Dataset Scope: Limited to PDM-Lite-CARLA scenarios
Recommended Use Cases
- ✅ CARLA simulation environments
- ✅ Research and development
- ✅ Autonomous driving prototyping
- ❌ Real-world deployment (requires additional training)
🛠️ Integration
This model is designed to work with:
- Baseer Self-Driving API - Live demo and API
- CARLA Simulator
- PyTorch Inference Pipeline
- Custom Autonomous Driving Systems
📚 Citation
If you use this model in your research, please cite:
@misc{interfuser-baseer-v1,
title={InterFuser-Baseer-v1: Fine-tuned Autonomous Driving Model},
author={BaseerAI},
year={2024},
publisher={Hugging Face},
howpublished={\url{https://huggingface.co/BaseerAI/Interfuser-Baseer-v1}}
}
👨💻 Development
Developed by: Adam Altawil
Project Type: Graduation Project - AI & Autonomous Driving
Institution: [Your Institution Name]
📄 License
This project is licensed under the MIT License - see the LICENSE file for details.
🤝 Contributing
Contributions, issues, and feature requests are welcome! Feel free to check the issues page.
📞 Support
For questions and support:
- Try the live demo: Baseer Server Space
- Create an issue in this repository
- Contact: [Your Contact Information]