Complete README.md for the model of YOLOTL

README.md

@@ -82,145 +82,94 @@ Fully Autonomous System: This model only recognizes lanes. It cannot be used to
 
 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
+## **Bias, Risks, and Limitations**
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+This model was designed for a specific purpose and environment, and thus has the following biases and limitations:
 
-[More Information Needed]
+* **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.
+* **Environmental Dependency:** It is biased towards clear weather and specific lighting conditions. Its lane recognition accuracy may decrease in rain, darkness, or strong backlight.
+* **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.
 
 ### Recommendations
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+To mitigate these risks and limitations, we recommend the following:
 
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+* **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.
+* **BEV Parameter Recalibration:** If the vehicle's camera is reinstalled or its position is altered, you **must** recalibrate the parameters for the BEV transformation.
+* **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.
 
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
+---
 
 ## Training Details
 
 ### Training Data
 
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
-
+* **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
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
+* **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.
+* **Training Hyperparameters:**
+    * model: `YOLOv8` (segmentation model)
+    * img_size: `640`
+    * conf_thres: `0.6`
+    * iou_thres: `0.5`
+    * epochs: `[Enter the number of epochs used for training here]`
+    * batch_size: `[Enter the batch size used for training here]`
+    * optimizer: `[Enter the optimizer used, e.g., AdamW]`
 
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
+---
 
 ## Evaluation
 
-<!-- This section describes the evaluation protocols and provides the results. -->
-
 ### Testing Data, Factors & Metrics
 
 #### Testing Data
 
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
+A separate dataset, captured from the same track environment but not used in training, was used for evaluation.
 
 #### Metrics
 
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
+**Intersection over Union (IoU)** was used as the primary metric to measure the overlap between the predicted lane masks and the ground truth masks.
 
 ### Results
 
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+* **mIoU (mean IoU):** `[Enter your final mIoU score on the test dataset here]`
+---
 
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
 
 ## Technical Specifications [optional]
 
 ### Model Architecture and Objective
 
-[More Information Needed]
+* **Architecture:** An **Instance Segmentation** model based on the YOLOv8 architecture.
+* **Objective:** To detect lanes as objects within an image and to accurately segment (mask) the pixel area corresponding to each lane.
 
 ### Compute Infrastructure
 
-[More Information Needed]
-
-#### Hardware
+* **Hardware:** `[Enter the GPU (e.g., NVIDIA RTX 3080) or CPU used for training and inference here]`
+* **Software:** `PyTorch`, `ultralytics`, `OpenCV`, `NumPy`, `ROS`
 
-[More Information Needed]
-
-#### Software
+---
 
-[More Information Needed]
 
 ## Citation [optional]
 
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
+If you find this model or code useful, please consider citing it as follows:
+```bibtex
+@misc{YourTeamName_YOLOTL_2025,
+  author = {[Your Team Name or Author Names]},
+  title = {YOLOv8 based Lane Segmentation Model for EV Autonomous Driving Competition},
+  year = {2025},
+  publisher = {Hugging Face},
+  journal = {Hugging Face repository},
+  howpublished = {\url{[Paste the Hugging Face URL of your model here]}},
+}
+## Model Card Authors
 
-[More Information Needed]
+Seungmin Lee
 
 ## Model Card Contact
 
-[More Information Needed]
+gmail: albert31115@gmail.com
+github: https://github.com/Highsky7