Update README.md

README.md

@@ -1,214 +1,231 @@
-Model Description
+# Bike Lane Detection Model (YOLOv11)
 
-This project uses a YOLOv11 object detection model to identify bike lane infrastructure and related objects in street images.
+## Model Description
 
-The model detects features such as bike lane markings, shared lanes, cyclists, and vehicles using bounding boxes and class labels. It was fine-tuned from a pre-trained YOLO model rather than trained from scratch, which allows it to learn from a relatively small dataset.
+This project uses a **YOLOv11 object detection model** to identify bike lane infrastructure and related objects in urban street images.
 
-The main goal of this project was not just to build a high-performing model, but to understand how well object detection works in this context and what limitations arise when working with real-world, imperfect data.
+The model detects features such as bike lane markings, shared lanes, cyclists, and vehicles using bounding boxes and class labels. It was fine-tuned from a pre-trained model rather than trained from scratch, which allows it to perform reasonably well even with a small dataset.
 
-Intended Use Cases:
+The goal of this project was not only to train a model, but to understand how dataset quality and structure affect performance in real-world computer vision tasks.
 
-Exploring bike lane infrastructure in street imagery
+**Intended Use Cases:**
+- Exploring bike lane infrastructure in street imagery  
+- Supporting transportation or urban planning research  
+- Analyzing cyclist environments and road conditions  
 
-Supporting transportation research
+This model is best suited for **research and learning purposes**, not real-world deployment.
 
-Analyzing road design and cyclist environments
+---
 
-This model is best suited for exploratory or research purposes rather than real-world deployment.
+## Training Data
 
-Training Data
+### Dataset Source
 
-Dataset Source:
-Roboflow Universe – Bike Lane Computer Vision Dataset
+Roboflow Universe – Bike Lane Computer Vision Dataset  
 
-The dataset consists of 147 images of urban street environments, including a mix of road layouts, traffic conditions, and lighting scenarios.
+---
 
-Classes and Distribution:
+### Dataset Overview
 
-Class	Count
-Vehicle	253
-Bicycle Lane	129
-Shared Dotted Lane	124
-Solid Lane	59
-Cyclist	13
-Bicycle	2
-Car	2
+The dataset contains **147 images** of urban street environments with varying road layouts, lighting conditions, and traffic scenarios.
 
-One of the most important characteristics of this dataset is strong class imbalance. Some classes, like vehicles and lane markings, appear frequently, while others like bicycles and cars have almost no examples. This has a direct impact on model performance.
+---
 
-Data Collection & Characteristics:
-Images represent real-world urban roads, primarily in daytime conditions, with varying visibility of lane markings and objects.
+### Class Distribution
 
-Annotation Process
+| Class | Count |
+|------|------|
+| Vehicle | 253 |
+| Bicycle Lane | 129 |
+| Shared Dotted Lane | 124 |
+| Solid Lane | 59 |
+| Cyclist | 13 |
+| Bicycle | 2 |
+| Car | 2 |
 
-The dataset included pre-existing YOLO-format bounding box annotations.
-
-Instead of creating new annotations, I focused on reviewing and validating the existing ones. I manually inspected a subset of images to check:
-
-whether bounding boxes aligned correctly with objects
-
-whether labels were applied consistently
-
-No major corrections were made. While this allowed me to focus on model training and evaluation, it also represents a limitation, since annotation quality was not improved or standardized further.
+This dataset shows **strong class imbalance**, where some classes appear very frequently while others have very few examples. This directly affects model performance.
 
-This is important because errors or inconsistencies in annotations can directly affect model performance, especially for less frequent classes.
+---
 
-Dataset Split
+### Annotation Process
 
-Train: 102 images (69%)
-
-Validation: 20 images (14%)
-
-Test: 16 images (11%)
-
-Data Augmentation
+The dataset included pre-existing YOLO-format bounding box annotations.
 
-Default YOLO augmentation techniques were used during training, including:
+I reviewed a subset of images to validate annotation quality, focusing on:
+- alignment of bounding boxes  
+- consistency of class labels  
 
-horizontal flipping
+No major corrections were made. This allowed me to focus on model training and evaluation, but it also represents a limitation since annotation quality was not significantly improved.
 
-color variation
+This project therefore emphasizes **evaluation and understanding of model performance** rather than dataset refinement.
 
-mosaic augmentation
+---
 
-Known Dataset Limitations
+### Dataset Split
 
-Significant class imbalance
+- Train: 102 images (69%)  
+- Validation: 20 images (14%)  
+- Test: 16 images (11%)  
 
-Extremely small number of examples for some classes
+---
 
-Limited dataset size overall
+### Data Augmentation
 
-Mostly urban, daytime conditions (lack of environmental diversity)
+Default YOLO augmentations were applied during training:
+- horizontal flipping  
+- color adjustments  
+- mosaic augmentation  
 
-Training Procedure
+---
 
-The model was trained using the Ultralytics YOLOv11 framework in Google Colab.
+### Known Dataset Limitations
 
-I fine-tuned a pre-trained model for 50 epochs using images resized to 640 × 640 pixels.
+- Strong class imbalance  
+- Extremely small sample sizes for some classes  
+- Limited total dataset size  
+- Mostly daytime, urban conditions  
 
-Training Details:
+---
 
-Framework: Ultralytics YOLOv11
+## Training Procedure
 
-Epochs: 50
+The model was trained using the **Ultralytics YOLOv11 framework** in Google Colab.
 
-Image size: 640
+Training used transfer learning, starting from a pre-trained model.
 
-Batch size: 16
+**Training Details:**
+- Framework: YOLOv11 (Ultralytics)  
+- Epochs: 50  
+- Batch size: 16  
+- Image size: 640 × 640  
+- Environment: Google Colab  
 
-Learning rate: default YOLO settings
+---
 
-Environment: Google Colab
+## Evaluation Results
 
-Training relied on transfer learning, which is especially useful given the small dataset size.
+### Key Metrics
 
-Evaluation Results
+- Precision: ~0.88  
+- Recall: ~0.38  
+- mAP50: ~0.48  
 
-Key Metrics:
+These metrics show that the model is **highly precise but has low recall**.
 
-Precision: ~0.88
+This means:
+- The model is usually correct when it makes predictions  
+- But it misses many objects, especially harder or less frequent ones  
 
-Recall: ~0.38
+---
 
-mAP50: ~0.48
+### Example Predictions
 
-Rather than focusing only on these numbers, it is more important to understand what they reveal about the model.
+![Prediction](./val_batch0_pred.jpg)
 
-The relatively high precision indicates that when the model makes a prediction, it is usually correct. However, the low recall suggests that the model is missing a significant number of objects.
+This example shows successful detection of lane markings and vehicles under clear conditions.
 
-This imbalance between precision and recall shows that the model is somewhat conservative — it avoids false positives but fails to detect more difficult or less frequent objects.
+---
 
-Per-Class Performance
+### Confusion Matrix
 
-Strong performance on common classes (vehicles, lane markings)
+![Confusion Matrix](./confusion_matrix.png)
 
-Weak performance on rare classes (bicycle, car)
+The confusion matrix highlights where the model struggles, particularly between similar lane types and rare classes.
 
-This is largely due to the extreme imbalance in the dataset.
+---
 
-Key Visualizations
+### Training Results
 
-![Confusion Matrix](./confusion_matrix.png)
 ![Training Results](./results.png)
-![Prediction Example](./val_batch0_pred.jpg)
-
-Performance Analysis
-
-The model performs best when:
 
-lane markings are clearly visible
+The training curve shows steady learning, but performance plateaus due to dataset limitations.
 
-lighting conditions are consistent
+---
 
-objects are not occluded
+### Failure Example
 
-However, the model struggles in several situations:
+![Failure Example](./failure_example.png)
 
-faded or worn bike lane markings
+This example shows a missed detection of a cyclist. This likely occurs due to:
+- small object size  
+- occlusion  
+- lack of sufficient training examples  
 
-overlapping or partially blocked objects
+---
 
-rare classes with very limited training data
+## Performance Analysis
 
-These results highlight that performance is not just about the model architecture, but heavily influenced by the dataset.
-
-In particular, the lack of examples for certain classes makes it difficult for the model to learn meaningful patterns.
+The model performs best when:
+- lane markings are clearly visible  
+- lighting conditions are consistent  
+- objects are large and unobstructed  
 
-Limitations and Biases
+The model struggles when:
+- markings are faded or unclear  
+- objects overlap or are partially blocked  
+- objects are small or rare in the dataset  
 
-This model has several important limitations that should be clearly acknowledged.
+This suggests that **dataset quality and balance are more important than model complexity** in this case.
 
-Failure Cases
+---
 
-missed detections of bicycles and cars
+## Limitations and Biases
 
-incorrect detections when lane markings are unclear
+### Failure Cases
 
-confusion between similar lane types
+- Missed detections of cyclists and small objects  
+- Confusion between similar lane types  
+- Reduced accuracy in cluttered scenes  
 
-Data Biases
+---
 
-overrepresentation of vehicles
+### Data Biases
 
-underrepresentation of rare classes
+- Overrepresentation of vehicles  
+- Underrepresentation of bicycles and cars  
+- Limited environmental diversity  
 
-limited diversity in environment and conditions
+---
 
-Environmental Limitations
+### Environmental Limitations
 
 The model may perform poorly under:
+- low lighting  
+- occlusion  
+- worn or faded lane markings  
 
-low lighting conditions
-
-occlusion
-
-faded or damaged road markings
+---
 
-Inappropriate Use Cases
+### Additional Observations
 
-This model should not be used for:
+The model sometimes misclassifies lane types (e.g., solid vs shared lanes) when markings are partially broken or unclear. This suggests the model relies heavily on strong visual patterns.
 
-real-time safety systems
+---
 
-autonomous driving
+### Inappropriate Use Cases
 
-decision-making in high-risk environments
+This model should **not** be used for:
+- autonomous driving systems  
+- real-time safety decisions  
+- high-risk environments  
 
-Sample Size Limitations
+---
 
-Some classes (such as bicycle and car) have extremely limited training data, making reliable detection difficult. This directly impacts recall and overall model performance.
+### Sample Size Limitations
 
-Final Reflection
+Some classes (e.g., bicycle and car) have extremely limited training data, making reliable detection difficult. This contributes directly to low recall.
 
-This project demonstrates that even with a strong model like YOLOv11, performance is highly dependent on the dataset.
+---
 
-Rather than focusing only on improving accuracy, this project highlights the importance of:
+## Final Reflection
 
-dataset quality
+This project demonstrates that model performance is heavily dependent on dataset quality.
 
-class balance
+Even with a strong model like YOLOv11, issues such as:
+- class imbalance  
+- small dataset size  
+- annotation limitations  
 
-annotation reliability
+can significantly impact results.
 
-Understanding these limitations is essential when applying computer vision models to real-world problems.
+Overall, this project highlights the importance of **data quality, not just model choice**, in computer vision applications.