Update README.md

README.md

@@ -1,3 +1,92 @@
----
-license: mit
----
+---
+license: mit
+language:
+- en
+metrics:
+- mean_iou
+pipeline_tag: object-detection
+tags:
+- LIDAR
+- Pointcloud
+- Autnonomous
+- YOLO
+---
+
+# **YOLOv12n LiDAR BEV Object Detection Model**  
+
+## **Model Overview**  
+This is a custom-trained **YOLOv12n** model for object detection on **Bird’s Eye View (BEV) RGB images** generated from **LiDAR 3D point cloud data**. The dataset used for training is derived from the **KITTI dataset**, converted from raw LiDAR point cloud data to 2D BEV images.  
+
+## **Dataset**  
+- **Source:** KITTI Dataset  
+- **Preprocessing:** LiDAR point clouds converted into **2D RGB BEV images**  
+- **Custom Labels:** Created for training  
+
+## **Training Details**  
+- **Training Platform:** Kaggle Notebook  
+- **Epochs:** 300 (Continual learning)  
+- **Batch Size:** 32  
+- **Input Image Size:** 608 × 608  
+- **Compute:** 2× NVIDIA T4 GPUs (Distributed Training)  
+- **Training Time:** 14.5 hours  
+- **Optimizer:** AdamW  
+
+## **Data Augmentation & Training Arguments**  
+The model was trained with the following augmentations and hyperparameters:  
+```python
+results = model.train(
+    data=os.path.join(Dataset_folder, "data.yaml"),
+    epochs=500,
+    imgsz=608,
+    plots=True,
+    batch=batch_size,
+    save=True,
+    save_period=100,
+    device="cuda",
+    workers=4,
+    project=Folder_name,
+    seed=2005,
+    copy_paste=0.15,
+    optimizer="AdamW",
+    mosaic=1.0,
+    scale=0.9,
+    verbose=True,
+    resume=True,
+    patience=100,
+    cache=True,
+    amp=True
+)
+```
+
+## **Usage**  
+To use this model for inference, load it using the Ultralytics YOLOv12 framework:  
+```python
+from ultralytics import YOLO
+
+model = YOLO("path/to/your/yolov12n.pt")
+results = model("path/to/your/image.jpg")
+results.show()
+```
+
+## **Performance & Applications**  
+- Designed for **autonomous driving** and **LiDAR-based perception**  
+- Capable of detecting objects from **BEV RGB images** derived from **3D LiDAR data**  
+- Suitable for **real-time object detection** in self-driving applications  
+
+## **License**  
+- mit
+  
+## **language**
+- english
+  
+## **metrics**
+- mean_iou
+  
+## **pipeline_tag**
+- object-detection
+  
+## **tags**
+- autonomous
+- selfdriving
+- LiDaR
+- Kitti