Update README.md

README.md

@@ -1,6 +1,6 @@
 # Model Description
 ### Overview
-This model detects hand gestures that can be used as input controls for video games. It uses object detection to recognize specific hand poses from a webcam or standard camera and translate them into game actions.
+This model detects hand gestures for use as input controls for video games. It uses object detection to recognize specific hand poses from a webcam or standard camera and translate them into game actions.
 The goal of the project is to explore whether computer vision–based gesture recognition can provide a low-cost and accessible alternative to traditional game controllers.
 
 ### Training Approach
@@ -13,4 +13,75 @@ The model was trained from pretrained YOLOv8n weights and fine-tuned on a custom
 * Interactive displays
 * Public kiosks
 * Smart home media controls
-* Desktop navigation
+* Desktop navigation
+
+# Training Data
+### Dataset Sources
+**The training dataset was constructed from two sources:**
+Rock-Paper-Scissors dataset
+* Source: Roboflow Universe
+* Creator: Audrey
+* Used for the first three gesture classes
+* Dataset URL: https://universe.roboflow.com/audrey-x3i6m/rps-knmjj
+
+Custom gesture dataset
+* Created by recording a 30-second video of the author performing gestures
+* Video parsed into frames at 10 frames per second
+* Images manually selected and annotated
+
+**Dataset Size**
+| Category         | Count     |
+| ---------------- | --------- |
+| Original Images  | 444       |
+| Augmented Images | 1066      |
+| Image Resolution | 512 × 512 |
+
+**Class Distribution**
+| Class    | Gesture     | Annotation Count |
+| -------- | ----------- | ---------------- |
+| Forward  | Open Palm   | 169              |
+| Backward | Closed Fist | 210              |
+| Jump     | Peace Sign  | 187              |
+| Attack   | Thumbs Up   | 121              |
+
+### Data Collection Methodology
+The dataset combines stock gesture images with a custom dataset created from recorded video frames.
+
+**The custom dataset was generated by:**
+* Recording a short gesture demonstration video
+* Extracting frames at 10 FPS
+* Selecting usable frames
+* Annotating gesture bounding boxes
+* This process produced 236 custom images that were merged with the stock dataset.
+
+### Annotation Process
+All annotations were created manually using Roboflow.
+Bounding boxes were drawn around the visible hand gesture in each image.
+Due to missing annotation metadata from the original dataset, all 444 images were annotated manually.
+Estimated annotation time: 2–3 hours
+
+### Train / Validation / Test Split
+| Dataset Split | Image Count |
+| ------------- | ----------- |
+| Training      | 933         |
+| Validation    | 88          |
+| Test          | 45          |
+
+### Data Augmentation
+**The following augmentations were applied:**
+* Rotation: ±15 degrees
+* Saturation adjustment: ±30%
+
+*These augmentations expanded the dataset from 444 to 1066 images.*
+
+### Dataset Availability
+Dataset availability: https://universe.roboflow.com/b-data-497-ws/hand-gesture-controls
+
+### Known Dataset Biases and Limitations
+* Small dataset size
+* Class imbalance (thumbs-up has fewer examples)
+* Mixed image quality between stock and custom images
+* Limited diversity in backgrounds and lighting conditions
+* Limited number of subjects (primarily one person)
+
+*These factors may affect model generalization.*