Update README.md

README.md

@@ -28,7 +28,17 @@ The working dataset is a subset of this,
 consisting of 9,616 images across 200 grocery product classes, sourced via 
 Roboflow 
 ([universe.roboflow.com/groceries-jxjfd/grocery-goods](https://universe.roboflow.com/groceries-jxjfd/grocery-goods)).
-This is the final dataset used for training, after the annotation process.
+
+### Annotation Process
+
+The original RPC-Dataset contained 200 product-specific classes,
+where each class represented a specific product variant (e.g., `100_milk`, `101_milk`, `102_milk`). These classes were collapsed into 17 broader product categories 
+to improve generalization, reduce class imbalance, and better reflect how a self-checkout system categorizes items by type rather than specific SKU. 
+For example, all milk classes were merged into a single `milk` class, reducing the total class count from 200 to 17. 
+Random samples were reviewed after relabeling to validate annotation quality, with no corrections needed.
+
+
+This is the final dataset used for training, after the annotation process
 ([https://app.roboflow.com/bdata-497-advanced-topics-in-dv-nqagm/grocery-goods-ezyyb](https://universe.roboflow.com/bdata-497-advanced-topics-in-dv-nqagm/grocery-goods-ezyyb)).
 
 ### Class Distribution
@@ -53,6 +63,29 @@ This is the final dataset used for training, after the annotation process.
 | gum               | 1,923       | 1,346          | 385              | 192        |
 | stationery        | 1,466       | 1,026          | 293              | 147        |
 
+### Train/Validation/Test Split
+| Split      | Ratio | Count  |
+| ---------- | ----- | ------ |
+| Train      | 70%   | 36,928 |
+| Validation | 20%   | 10,505 |
+| Test       | 10%   | 5,276  |
+
+### Data Augmentation
+The following augmentations were applied during training to simulate real-world checkout conditions:
+| Augmentation                       | Purpose                                 |
+| ---------------------------------- | --------------------------------------- |
+| Rotation                           | Items placed on belt in any orientation |
+| Horizontal/Vertical Flip           | Additional orientation variation        |
+| Mosaic                             | Multiple items on belt simultaneously   |
+| HSV Shift (hue, saturation, value) | Simulate varied store lighting          |
+| Translation & Scale                | Camera height and position variation    |
+
+### Known Biases and Limitations
+- Dataset is predominantly composed of Chinese grocery product packaging, limiting generalizability to Western or European retail environments
+- Fresh and unpackaged produce (such as fruits or vegetables) are not represented in the dataset
+- Limited lighting variation — real checkout environments may have inconsistent lighting not well represented in training images
+
+
 ## Training Procedure
 
 - **Architecture**: YOLOv11n