Update README.md

README.md

@@ -10,20 +10,14 @@ pipeline_tag: image-segmentation
 ---
 # Model Card for Model ID
 
-<!-- Provide a quick summary of what the model is/does. -->
-
 This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
 
 ## Model Details
 
 ### Model Description
 
-<!-- Provide a longer summary of what this model is. -->
-
-
-
 - Developed by:Highsky7
-- Model type:YOLOv8
+- Model type:Image-segmentation
 - License:MIT
 
 ### Model Sources [optional]
@@ -34,25 +28,59 @@ This modelcard aims to be a base template for new models. It has been generated
 
 ## Uses
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+This model is a lane recognition model created for use by Konkuk University Team 2 in The 4th International University Student EV Autonomous Driving Competition (2025-07-11). The accompanying roboflow_final.py code is a ROS-based driving node that utilizes this model to control the vehicle's steering angle in real-time.
 
 ### Direct Use
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+This model can be used directly via the ultralytics library.
+
+First, install the necessary libraries:
+
+Bash
+
+pip install ultralytics opencv-python
+The following is example code to load the model from Hugging Face and perform lane recognition on a single image.
+
+Python
+
+from ultralytics import YOLO
+import cv2
+
+model = YOLO("YourUsername/YourModelName") 
+
+image_path = "path/to/your/test_image.jpg"
+frame = cv2.imread(image_path)
+
+results = model(frame, conf=0.6)
+
+result_plot = results[0].plot()
+
+cv2.imshow("Lane Detection Result", result_plot)
+cv2.waitKey(0)
+cv2.destroyAllWindows()
 
-[More Information Needed]
 
 ### Downstream Use [optional]
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+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:
 
-[More Information Needed]
+Generates a drivable center path based on the detected left/right lanes.
+
+Calculates a dynamic lookahead point based on the generated path and the vehicle's speed (throttle).
+
+Determines the steering angle using the Pure Pursuit algorithm, based on the lookahead point and the vehicle's current position.
+
+Interfaces with other modules, such as triggering a forced RRT algorithm when an obstacle (cone) is detected on the driving path.
 
 ### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+This model was designed for a specific purpose and environment. Its use in the following situations may be inappropriate or dangerous:
 
-[More Information Needed]
+General Road Driving: Performance is not guaranteed on public roads with different lane markings, lighting, and weather conditions than those of the competition track.
+
+Fully Autonomous System: This model only recognizes lanes. It cannot be used to build a complete autonomous system on its own, as it does not detect pedestrians, other vehicles, traffic lights, or signs.
+
+Changes in Camera Setup: The model's Bird's-Eye-View (BEV) transformation logic is calibrated for a specific camera position and angle, stored in the bev_params_y_5.npz file. If the camera's mounting position or angle is changed, the coordinate transformation will be inaccurate, severely degrading model performance.
 
 ## Bias, Risks, and Limitations