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--- |
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license: mit |
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datasets: |
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- Highsky7/Topview_Lane |
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language: |
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- en |
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base_model: |
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- Ultralytics/YOLOv8 |
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pipeline_tag: image-segmentation |
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new_version: Ultralytics/YOLOv8 |
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tags: |
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- lane |
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--- |
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# Model Card for Model ID |
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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). |
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## Model Details |
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### Model Description |
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- Developed by:Highsky7 |
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- Model type:Image-segmentation |
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- License:MIT |
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### Model Sources |
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https://github.com/Highsky7/YOLOTL |
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- **Repository:** |
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## Uses |
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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. |
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### Direct Use |
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This model can be used directly via the ultralytics library. |
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First, install the necessary libraries: |
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Bash |
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pip install ultralytics opencv-python |
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The following is example code to load the model from Hugging Face and perform lane recognition on a single image. |
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Python |
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from ultralytics import YOLO |
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import cv2 |
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model = YOLO("YourUsername/YourModelName") |
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image_path = "path/to/your/test_image.jpg" |
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frame = cv2.imread(image_path) |
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results = model(frame, conf=0.6) |
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result_plot = results[0].plot() |
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cv2.imshow("Lane Detection Result", result_plot) |
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cv2.waitKey(0) |
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cv2.destroyAllWindows() |
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### Downstream Use |
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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: |
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Generates a drivable center path based on the detected left/right lanes. |
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Calculates a dynamic lookahead point based on the generated path and the vehicle's speed (throttle). |
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Determines the steering angle using the Pure Pursuit algorithm, based on the lookahead point and the vehicle's current position. |
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Interfaces with other modules, such as triggering a forced RRT algorithm when an obstacle (cone) is detected on the driving path. |
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### Out-of-Scope Use |
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This model was designed for a specific purpose and environment. Its use in the following situations may be inappropriate or dangerous: |
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General Road Driving: Performance is not guaranteed on public roads with different lane markings, lighting, and weather conditions than those of the competition track. |
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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. |
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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. |
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## **Bias, Risks, and Limitations** |
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This model was designed for a specific purpose and environment, and thus has the following biases and limitations: |
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* **Data Bias:** The model was trained exclusively on data filmed on **'The International University Student EV Autonomous Driving Competition' track**. Consequently, its performance may degrade significantly on public roads with different lane shapes, colors, or lighting conditions. |
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* **Environmental Dependency:** It is biased towards clear weather and specific lighting conditions. Its lane recognition accuracy may decrease in rain, darkness, or strong backlight. |
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* **Hardware Dependency:** The model's core logic, the Bird's-Eye-View (BEV) transform, is highly dependent on a **fixed camera setup** (position and angle) defined in `bev_params_y_5.npz`. Any change to the camera's position or angle will invalidate the coordinate system and cause the model to fail. |
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### Recommendations |
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To mitigate these risks and limitations, we recommend the following: |
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* **Restricted Use:** This model is intended for use in environments similar to the training data (i.e., the competition track). It is not suitable for general road driving or other projects. |
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* **BEV Parameter Recalibration:** If the vehicle's camera is reinstalled or its position is altered, you **must** recalibrate the parameters for the BEV transformation. |
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* **Safety Mechanisms:** When applying this model to a physical vehicle, it is crucial to implement safety mechanisms such as a **manual override system** or an **emergency stop system** to handle prediction failures. |
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--- |
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## Training Details |
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### Training Data |
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* **Dataset:** A custom dataset of driving images captured on 'The International University Student EV Autonomous Driving Competition' track. |
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* **Labeling:** The left and right lane areas in the images were labeled with **segmentation masks**. |
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### Training Procedure |
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* **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. |
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* **Training Hyperparameters:** |
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* model: `YOLOv8` (segmentation model) |
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* img_size: `640` |
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* conf_thres: `0.6` |
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* iou_thres: `0.5` |
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* epochs: `[Enter the number of epochs used for training here]` |
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* batch_size: `[Enter the batch size used for training here]` |
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* optimizer: `[Enter the optimizer used, e.g., AdamW]` |
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--- |
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## Evaluation |
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### Testing Data, Factors & Metrics |
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#### Testing Data |
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A separate dataset, captured from the same track environment but not used in training, was used for evaluation. |
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#### Metrics |
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**Intersection over Union (IoU)** was used as the primary metric to measure the overlap between the predicted lane masks and the ground truth masks. |
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### Results |
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* **mIoU (mean IoU):** `[Enter your final mIoU score on the test dataset here]` |
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--- |
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## Technical Specifications |
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### Model Architecture and Objective |
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* **Architecture:** An **Instance Segmentation** model based on the YOLOv8 architecture. |
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* **Objective:** To detect lanes as objects within an image and to accurately segment (mask) the pixel area corresponding to each lane. |
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### Compute Infrastructure |
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* **Hardware:** `[Enter the GPU (e.g., NVIDIA RTX 3080) or CPU used for training and inference here]` |
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* **Software:** `PyTorch`, `ultralytics`, `OpenCV`, `NumPy`, `ROS` |
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--- |
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## Citation |
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If you find this model or code useful, please consider citing it as follows: |
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```bibtex |
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@misc{YourTeamName_YOLOTL_2025, |
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author = {[Your Team Name or Author Names]}, |
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title = {YOLOv8 based Lane Segmentation Model for EV Autonomous Driving Competition}, |
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year = {2025}, |
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publisher = {Hugging Face}, |
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journal = {Hugging Face repository}, |
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howpublished = {\url{[Paste the Hugging Face URL of your model here]}}, |
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} |
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## Model Card Authors |
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Seungmin Lee |
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## Model Card Contact |
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gmail: albert31115@gmail.com |
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github: https://github.com/Highsky7 |
