Update README.md

README.md

@@ -22,21 +22,14 @@ configs:
       - "valid/labels/*"
 ---
 
-# 🌊 Wavez Surfing Dataset
+# 🌊 Wavez Surfing Dataset (WAVEZ PRO)
 
-## 📝 Dataset Explanation
-This dataset was specifically curated for the **WAVEZ PRO** venture. It is designed to train object detection models (specifically YOLOv8) to identify surfers and surfboards in real-time coastal camera feeds. The primary goal is to provide accurate crowd density metrics at various surf spots to help surfers find the best time to hit the water.
+## 📝 Explanation
+This dataset is a core component of the **WAVEZ PRO** venture. It is designed to monitor crowd density at surfing spots by detecting surfers and surfboards in real-time. This project aims to help the surfing community find the best time to hit the water based on live data.
 
 ## 📊 Exploratory Data Analysis (EDA)
-To address the project requirements, here is a summary of the dataset characteristics:
-
-* **Total Images:** Approximately 8,000 images curated from various surf spot cameras.
-* **Class Distribution:**
-  * **Surfer:** The primary class, representing individuals in the water.
-* **Data Split:**
-  * **Training Set:** 80% of the data used for model fine-tuning.
-  * **Validation Set:** 20% used for evaluating model performance during training.
-* **Image Quality:** Standard 640x640 resolution to ensure compatibility with YOLOv8 architectures.
-
-## 🛠️ Usage
-The labels are provided in standard YOLO format (.txt files) corresponding to each image. Once the Dataset Viewer is active, you will be able to see the bounding box overlays directly on the images in this repository.
+* **Total Images/Rows:** 8,194.
+* **Data Split:** 80% Training (~6,555 images), 20% Validation (~1,639 images).
+* **Classes:** `Surfer`
+* **Annotation Format:** YOLO PyTorch (normalized bounding boxes).
+* **Image Specs:** 640x640 resolution, optimized for YOLOv8 fine-tuning.