Update README.md

README.md

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----
-license: mit
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+---
+license: mit
+tags:
+- point-cloud
+- semantic-segmentation
+- autonomous-driving
+- lane-detection
+---
+
+# Model Card for Point Transformer V3 Lane Detection
+
+This model performs semantic segmentation (lane line) on LiDAR point cloud data to detect and segment lane markings for autonomous vehicle navigation.
+
+## Model Details
+
+### Model Description
+
+Point Transformer V3 model adapted for lane detection from LiDAR point clouds, featuring hierarchical encoder-decoder architecture with self-attention mechanisms for point cloud processing.
+
+- **Developed by:** Bryan Chang
+- **Model type:** Point Transformer V3 (PT-v3m1)
+- **License:** MIT
+- **Finetuned from model:** Nuscence-pretrained model
+
+### Model Sources
+
+- **Repository:** https://github.com/Bryan1203/LiDAR-Based-Lane-Navigation
+- **Demo:** https://www.youtube.com/watch?v=cCTi2zFftlY
+
+## Uses
+
+### Direct Use
+
+The model can be directly used for:
+- Lane detection from LiDAR point cloud data (ouster lidar with signal attribute)
+- Semantic segmentation of road surfaces
+- Real-time autonomous navigation systems
+
+### Downstream Use
+
+Can be integrated into:
+- Autonomous vehicle navigation systems
+- Road infrastructure mapping
+- Traffic monitoring systems
+- Path planning algorithms
+
+### Out-of-Scope Use
+
+This model should not be used for:
+- Non-LiDAR point cloud data
+- Indoor navigation
+- Object detection tasks
+- High-speed autonomous driving without additional safety systems
+
+## Bias, Risks, and Limitations
+
+- Performance may degrade in adverse weather conditions
+- Requires high-quality LiDAR data
+- Limited to ground-level lane markings
+- May struggle with unusual road geometries
+- Real-time performance depends on hardware capabilities
+
+### Recommendations
+
+Users should:
+- Validate model performance in their specific deployment environment
+- Implement appropriate safety fallbacks
+- Consider sensor fusion for robust operation
+- Monitor inference time for real-time applications
+- Regularly evaluate model performance on new data
+
+## How to Get Started with the Model
+
+refer to the repo, src/pointcept151/inference_ros_filter.py for implementation
+
+## Training Details
+
+### Training Data
+
+- Based on SemanticKITTI dataset format
+- Binary classification: background (0) and lane (1)
+- Point cloud data with 4 channels: x, y, z, intensity (signal)
+
+### Training Procedure
+
+#### Preprocessing
+- Grid sampling with size 0.05
+- Random rotation, scaling, and flipping augmentations
+- Random jittering (σ=0.005, clip=0.02)
+
+#### Training Hyperparameters
+
+- **Training regime:** Mixed precision (fp16)
+- Batch size: 4
+- Epochs: 50
+- Optimizer: AdamW (lr=0.004, weight_decay=0.005)
+- Scheduler: OneCycleLR
+- Loss functions: CrossEntropy + Lovasz Loss
+
+#### Speeds, Sizes, Times
+
+- Inference time: 300-400ms per frame on RTX A4000
+- Model size: ~500MB
+- Training time: ~24 hours on single GPU
+
+## Evaluation
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+- Custom labeled high-bay dataset (UIUC testing facility)
+- Test split from training data
+
+#### Factors
+- Time of day
+- Weather conditions
+- Road surface types
+- Lane marking visibility
+
+#### Metrics
+- Mean IoU
+- Per-class accuracy
+- Inference time
+- Memory usage
+
+### Results
+
+Performance metrics on test set:
+- Mean IoU: [Pending final evaluation]
+- Background accuracy: [Pending final evaluation]
+- Lane accuracy: [Pending final evaluation]
+
+## Environmental Impact
+
+- **Hardware Type:** NVIDIA RTX A4000
+- **Hours used:** ~24 for training
+- **Cloud Provider:** Local computation
+- **Carbon Emitted:** [To be calculated]
+
+## Technical Specifications
+
+### Model Architecture and Objective
+
+Detailed in configuration:
+- Encoder depths: (2, 2, 2, 6, 2)
+- Encoder channels: (32, 64, 128, 256, 512)
+- Decoder depths: (2, 2, 2, 2)
+- MLP ratio: 4
+- Attention heads: Varies by layer
+
+### Compute Infrastructure
+
+#### Hardware
+- NVIDIA RTX A4000 (16GB VRAM)
+- 32GB RAM minimum
+- Multi-core CPU
+
+#### Software
+- Python 3.8+
+- PyTorch 1.10+
+- CUDA 11.3+
+- ROS Noetic
+- Pointcept framework
+
+## Model Card Authors
+
+Bryan Chang
+
+## Model Card Contact
+
+bryanchang1234@gmail.com