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-You got it. Here is the complete, professional, and copy-paste-ready README.md content in English for your model repository on the Hugging Face Hub.
-
-This version is structured to be clear, informative, and follows the best practices for creating a Model Card.
-
-README.md for Adam-IT/Interfuser-Baseer-v1
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-# 🚗 InterFuser Model for Autonomous Driving (Fine-tuned for Baseer API)
+# 🚗 InterFuser-Baseer-v1: Autonomous Driving Model
 
-## 📖 Model Description
+[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
+[![PyTorch](https://img.shields.io/badge/PyTorch-EE4C2C?style=flat&logo=pytorch&logoColor=white)](https://pytorch.org/)
+[![CARLA](https://img.shields.io/badge/CARLA-Simulator-blue)](https://carla.org/)
 
-This repository contains the fine-tuned weights for the **InterFuser** model, a state-of-the-art transformer-based architecture for autonomous driving. This specific version has been meticulously fine-tuned to power the **[Baseer Self-Driving API](https://huggingface.co/spaces/Adam-IT/Baseer_Server)**, with a primary focus on robust traffic object detection and safe trajectory planning within the CARLA simulation environment.
+## 📖 Overview
 
-The model processes a single front-facing camera view and vehicle state measurements to produce a comprehensive understanding of the driving scene. It simultaneously predicts the future path of the ego-vehicle while detecting and classifying surrounding traffic participants.
+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.
 
-This model serves as the core "brain" for the Baseer project, demonstrating an end-to-end approach to autonomous driving perception and planning.
+### 🎯 Key Capabilities
 
-## ✨ Key Features
+- **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
 
-*   **Transformer-Based Fusion:** Leverages the power of transformers to effectively fuse image features with vehicle state information.
-*   **Multi-Task Learning:** Simultaneously performs two critical tasks:
-    1.  **Traffic Object Detection:** Identifies cars, motorcycles, and pedestrians in a 20x20 meter grid in front of the vehicle.
-    2.  **Waypoint Prediction:** Predicts a safe and drivable trajectory for the next 10 waypoints.
-*   **Scene Understanding:** Provides logits for crucial environmental factors, including the presence of junctions, red light hazards, and stop signs.
-*   **Optimized for CARLA:** Fine-tuned on the `PDM_Lite_Carla` dataset, making it highly effective for scenarios within the CARLA simulator.
+## 🏗️ Architecture
 
-## 🛠️ Model Architecture
+### Model Components
 
-This is a variant of the original InterFuser architecture with the following specifications:
+| 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 |
 
-*   **Image Backbone:** `ResNet-50` (pretrained on ImageNet)
-*   **LiDAR Backbone:** `ResNet-18` (architecture defined, but LiDAR input is disabled in this version)
-*   **Transformer:**
-    *   **Embedding Dimension:** 256
-    *   **Encoder Depth:** 6 Layers
-    *   **Decoder Depth:** 6 Layers
-    *   **Attention Heads:** 8
-*   **Prediction Heads:**
-    *   **Waypoints:** Gated Recurrent Unit (GRU) based predictor.
-    *   **Traffic Detection:** A detection head that outputs a `20x20x7` grid representing object confidence, position offsets, dimensions, and orientation.
+### Output Format
 
-## 🚀 How to Use
+- **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
 
-This model is intended to be used as part of the **Baseer Self-Driving API**. However, you can load it directly in PyTorch for your own projects.
+## 🚀 Quick Start
+
+### Installation
 
-**1. Installation**
 ```bash
 pip install torch torchvision timm huggingface_hub
-# You will also need the model definition class (Interfuser) and helper functions from the project.
-
+```
 
-2. Loading the Model
-The recommended way to load the model is by using the custom load_and_prepare_model function from the project, which handles the configuration and weight loading automatically.
+### Usage Example
 
-Generated python
+```python
 import torch
-# Assuming your helper functions are in a file named 'config_loader.py'
-from config_loader import load_and_prepare_model 
+from huggingface_hub import hf_hub_download
 
-# Set the device
+# Download model weights
+model_path = hf_hub_download(
+    repo_id="Adam-IT/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
 
-# Load the model from the Hub
-# The function will automatically download from "Adam-IT/Interfuser-Baseer-v1"
-try:
-    model = load_and_prepare_model(device)
-    model.eval()
-    print("Model loaded successfully!")
-except Exception as e:
-    print(f"Error loading model: {e}")
+- **Dataset**: PDM-Lite-CARLA (Urban driving scenarios)
+- **Training Objective**: Multi-task learning with IoU optimization
+- **Framework**: PyTorch
 
-# Now you can use the model for inference
-# dummy_input = ...
-# with torch.no_grad():
-#     outputs = model(dummy_input)
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-📈 Training and Fine-tuning
+### Key Metrics
 
-This model was fine-tuned from a pretrained InterFuser checkpoint.
+- Optimized for traffic detection accuracy
+- Enhanced bounding box IoU performance
+- Robust waypoint prediction in urban scenarios
 
-Dataset: PDM_Lite_Carla, a dataset generated using the CARLA simulator, focusing on diverse urban driving scenarios.
+## ⚠️ Limitations
 
-Training Objective: The fine-tuning process prioritized the traffic detection task. The loss function was weighted heavily towards improving the Intersection over Union (IoU) of predicted bounding boxes and the accuracy of the traffic map.
+### Current Constraints
 
-Framework: PyTorch
+- **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
 
-Training Run: Finetune_Focus_on_Detection_v5
+### Recommended Use Cases
 
-⚠️ Limitations and Bias
+- ✅ CARLA simulation environments
+- ✅ Research and development
+- ✅ Autonomous driving prototyping
+- ❌ Real-world deployment (requires additional training)
 
-Simulation-Only: This model is trained exclusively on simulated data from CARLA. Its performance in real-world scenarios is untested and likely to be poor without significant domain adaptation and further training on real-world datasets.
+## 🛠️ Integration
 
-Single Camera View: The model relies solely on a front-facing camera. It has blind spots and cannot perceive objects to the sides or rear of the vehicle.
+This model is designed to work with:
 
-No LiDAR: Although the architecture supports LiDAR, this version was trained without it. It may struggle in adverse weather conditions (e.g., rain, fog) or poor lighting where vision is compromised.
+- **Baseer Self-Driving API**
+- **CARLA Simulator**
+- **PyTorch Inference Pipeline**
+- **Custom Autonomous Driving Systems**
 
-Dataset Bias: The model's behavior is limited by the scenarios present in the PDM_Lite_Carla dataset. It may not handle rare or "out-of-distribution" events correctly.
+## 📚 Citation
 
-👨‍💻 Developed By
+If you use this model in your research, please cite:
 
-Adam-IT
+```bibtex
+@misc{interfuser-baseer-v1,
+  title={InterFuser-Baseer-v1: Fine-tuned Autonomous Driving Model},
+  author={Adam-IT},
+  year={2024},
+  publisher={Hugging Face},
+  howpublished={\url{https://huggingface.co/Adam-IT/Interfuser-Baseer-v1}}
+}
+```
 
-This model is a core component of a graduation project in the field of Artificial Intelligence and Autonomous Driving.
+## 👨‍💻 Development
 
-📜 License
+**Developed by**: Adam-IT  
+**Project Type**: Graduation Project - AI & Autonomous Driving  
+**Institution**: [Your Institution Name]
 
-This project is licensed under the MIT License. See the LICENSE file for more details.
+## 📄 License
+
+This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
+
+## 🤝 Contributing
+
+Contributions, issues, and feature requests are welcome! Feel free to check the [issues page](../../issues).
+
+## 📞 Support
+
+For questions and support:
+- Create an issue in this repository
+- Contact: [Your Contact Information]
+
+---
 
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+  <strong>🚗 Drive the Future with AI 🚗</strong>
+</div>