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README.md

@@ -4,152 +4,52 @@ tags:
 - autonomous-driving
 - planning
 - pytorch
-- navsim
-- transfuser
-- end-to-end-driving
-library_name: pytorch
 ---
 
-# TFv6 NavSim - Autonomous Driving Planning Model
+# TFv6 NavSim
 
-## Model Description
+Autonomous driving planning model (TransFuser-based). Predicts waypoints and headings.
 
-TFv6 NavSim is an end-to-end autonomous driving planning model based on the TransFuser architecture. The model predicts future waypoints and vehicle headings for trajectory planning in autonomous driving scenarios.
-
-**Key Features:**
-- 🚗 End-to-end learning for autonomous driving
-- 📷 Multi-camera input processing (4 cameras)
-- 🎯 Predicts future waypoints and headings
-- 🏎️ Trained on NavSim dataset
-- ⚡ Efficient inference with mixed precision support
-
-**Architecture:**
-- Backbone: TransFuser with vision encoder
-- Planning Decoder: GPT-based trajectory prediction
-- Input: RGB images (1600x900), navigation commands, speed, acceleration
-- Output: Future waypoints and heading predictions
-
-## Quick Start
-
-### Installation
+## Usage
 
 ```bash
-pip install torch torchvision timm numpy opencv-python jaxtyping beartype omegaconf huggingface_hub
+pip install torch timm numpy opencv-python jaxtyping beartype omegaconf huggingface_hub
 ```
 
-### Simple Inference
+### Quick Start
 
 ```python
-from huggingface_hub import hf_hub_download
 from inference import TFv6NavSimInference
 import numpy as np
 
-# Download and load model
-model_path = hf_hub_download(repo_id="longpollehn/tfv6_navsim", filename="model_0060.pth")
-model = TFv6NavSimInference(model_path)
+# Auto-downloads from HuggingFace
+model = TFv6NavSimInference()
 
-# Prepare input (example with dummy data)
-rgb = np.random.randint(0, 255, (900, 1600, 3), dtype=np.uint8)  # HWC format
+# Prepare input
+rgb = np.random.randint(0, 255, (900, 1600, 3), dtype=np.uint8)
 command = [0, 0, 1, 0]  # [left, right, straight, lanefollow]
 speed = 5.0  # m/s
 acceleration = 0.0  # m/s²
 
 # Run inference
 result = model.predict(rgb, command, speed, acceleration)
-print(f"Predicted waypoints: {result['waypoints'].shape}")
-print(f"Predicted headings: {result['headings'].shape}")
-```
-
-### Inference from Image File
-
-```python
-result = model.predict_from_image_path(
-    "path/to/image.jpg",
-    command=[0, 0, 1, 0],  # Go straight
-    speed=5.0,
-    acceleration=0.0
-)
-```
-
-## Detailed Usage
-
-### Input Format
-
-**RGB Image:**
-- Shape: `(3, H, W)` or `(H, W, 3)`
-- Expected size: 1600x900 pixels
-- Range: [0, 255] (will be normalized internally)
-
-**Navigation Command:**
-- 4-element array: `[left, right, straight, lanefollow]`
-- Values typically between 0 and 1
-- Examples:
-  - Turn left: `[1, 0, 0, 0]`
-  - Go straight: `[0, 0, 1, 0]`
-  - Turn right: `[0, 1, 0, 0]`
-  - Lane follow: `[0, 0, 0, 1]`
-
-**Speed:** Current vehicle speed in meters per second (m/s)
-
-**Acceleration:** Current vehicle acceleration in m/s²
-
-### Output Format
-
-Returns a dictionary with:
-- `waypoints`: numpy array of shape `(N, 2)` - predicted (x, y) positions
-- `headings`: numpy array of shape `(N,)` - predicted heading angles
-
-## Model Details
-
-### Training Configuration
-- Dataset: NavSim with 4-camera setup
-- Batch size: 64
-- Learning rate: 0.0003
-- Mixed precision training: Enabled
-- Input resolution: 1600x900 (per camera)
-- BEV grid: 256x256 pixels (64x64 meters, 4 pixels/meter)
-
-### Performance
-- Trained for 61 epochs
-- Checkpoint: model_0060.pth
-
-## Gradio Demo
-
-A Gradio web interface is available in `app.py`:
-
-```bash
-pip install gradio
-python app.py
+print(result['waypoints'].shape, result['headings'].shape)
 ```
 
-Then open the provided URL in your browser.
-
-## Files in this Repository
-
-- `model_0060.pth` - Model checkpoint weights
-- `config.json` - Model configuration
-- `stand_alone_model.py` - Model architecture implementation
-- `inference.py` - Easy-to-use inference wrapper
-- `app.py` - Gradio web demo
-- `requirements.txt` - Python dependencies
-
-## Citation
-
-If you use this model in your research, please cite:
-
-```bibtex
-@misc{tfv6_navsim,
-  title={TFv6 NavSim - Autonomous Driving Planning Model},
-  author={Long Nguyen},
-  year={2025},
-  url={https://huggingface.co/longpollehn/tfv6_navsim}
-}
-```
+## Input/Output
 
-## License
+**Input:**
+- RGB: (900, 1600, 3) or (3, 900, 1600), range [0, 255]
+- Command: [left, right, straight, lanefollow], e.g. [0,0,1,0] for straight
+- Speed: m/s
+- Acceleration: m/s²
 
-Apache 2.0
+**Output:**
+- `waypoints`: (N, 2) predicted positions
+- `headings`: (N,) predicted angles
 
-## Acknowledgments
+## Details
 
-This model is based on the TransFuser architecture and trained on the NavSim dataset.
+- Architecture: TransFuser
+- Dataset: NavSim (4 cameras)
+- Checkpoint: Epoch 60