Update README.md

README.md

@@ -20,7 +20,7 @@ This modelcard aims to be a base template for new models. It has been generated
 - Model type:Image-segmentation
 - License:MIT
 
-### Model Sources [optional]
+### Model Sources
 
 https://github.com/Highsky7/YOLOTL
 
@@ -59,8 +59,7 @@ cv2.imshow("Lane Detection Result", result_plot)
 cv2.waitKey(0)
 cv2.destroyAllWindows()
 
-
-### Downstream Use [optional]
+### Downstream Use
 
 The output of this model (lane masks) can be used as a key input for a larger autonomous driving system. For example, the roboflow_final.py code performs the following downstream tasks:
 
@@ -106,6 +105,7 @@ To mitigate these risks and limitations, we recommend the following:
 
 * **Dataset:** A custom dataset of driving images captured on 'The International University Student EV Autonomous Driving Competition' track.
 * **Labeling:** The left and right lane areas in the images were labeled with **segmentation masks**.
+
 ### Training Procedure
 
 * **Preprocessing:** Original images were transformed into 2D top-down Bird's-Eye-View (BEV) images using fixed parameters (`bev_params_y_5.npz`) before being used for training. This helps the model recognize lanes from a top-down perspective, facilitating distance calculation and path planning.
@@ -137,8 +137,7 @@ A separate dataset, captured from the same track environment but not used in tra
 * **mIoU (mean IoU):** `[Enter your final mIoU score on the test dataset here]`
 ---
 
-
-## Technical Specifications [optional]
+## Technical Specifications
 
 ### Model Architecture and Objective
 
@@ -152,8 +151,7 @@ A separate dataset, captured from the same track environment but not used in tra
 
 ---
 
-
-## Citation [optional]
+## Citation
 
 If you find this model or code useful, please consider citing it as follows:
 ```bibtex