Update README.md

README.md

@@ -5,6 +5,8 @@ By Dylan Dang
 
 This project uses a **YOLOv11 object detection model** to identify bike lane infrastructure and related objects in urban street images.
 
+YOLOv11 was chosen for its balance of speed and accuracy in real-time object detection tasks.
+
 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.
 
 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.
@@ -51,6 +53,7 @@ This dataset shows **strong class imbalance**, where some classes appear very fr
 ### Annotation Process
 
 The dataset included pre-existing YOLO-format bounding box annotations.
+Although the dataset was pre-annotated, I reviewed samples to check for consistency and quality. I observed that some classes (such as "car" and "vehicle") overlap conceptually, which may introduce ambiguity during training. No major corrections were made, but this overlap influenced how results were interpreted.
 
 I reviewed a subset of images to validate annotation quality, focusing on:
 - alignment of bounding boxes  
@@ -92,7 +95,8 @@ Default YOLO augmentations were applied during training:
 
 The model was trained using the **Ultralytics YOLOv11 framework** in Google Colab.
 
-Training used transfer learning, starting from a pre-trained model.
+Training was performed in Google Colab using an RTX 3070. Training took approximately 5-10 minutes.
+
 
 **Training Details:**
 - Framework: YOLOv11 (Ultralytics)  
@@ -117,6 +121,8 @@ This means:
 - The model is usually correct when it makes predictions  
 - But it misses many objects, especially harder or less frequent ones  
 
+Performance varied significantly across classes. Common classes such as "Vehicle" achieved higher precision and recall, while underrepresented classes like "Bicycle" and "Car" performed poorly due to limited training samples.
+
 ---
 
 ### Example Predictions