Update README.md

README.md

@@ -18,14 +18,14 @@ verification, and loss/theft prevention. This model is best suited for stores
 whose inventory closely mathes the training data, as performance will degrade
 on unseen brands or product types not represented in the training data.
 
-
 ## Training Data
 
 The training dataset is a subset of the **RPC-Dataset** 
 ([rpc-dataset.github.io](https://rpc-dataset.github.io/)), 
-a large-scale retail product checkout dataset. 
+a large-scale retail product checkout dataset consisting of 83699 images
+across 200 grocery product classes. 
 The working dataset is a subset of this,
-consisting of 9,616 images across 200 grocery product classes, sourced via 
+consisting of 9,616 images across the same 200 classes, sourced via 
 Roboflow 
 ([universe.roboflow.com/groceries-jxjfd/grocery-goods](https://universe.roboflow.com/groceries-jxjfd/grocery-goods)).
 
@@ -37,7 +37,6 @@ to improve generalization, reduce class imbalance, and better reflect how a self
 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)).
 
@@ -85,20 +84,31 @@ The following augmentations were applied during training to simulate real-world
 - 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
-- **Layers**: 
-- **Parameters**: 
-- **Gradients**: 
-- **GFLOPs**: 
-- **Classes (nc=)**:
-  1. 
-  2. 
-  3.
-  4. 
-  5. 
-  6. 
-  7. 
-  8. 
+- **Framework**: Ultralytics YOLOv11n
+- **Hardware**: A100 GPU in Google Colab
+- **Epochs**: 50
+- **Batch Size**: 64
+- **Image Size**: 640x640 
+- **Patience**: 50
+- **Training Time** ~36.5 minutes (2,189.69 seconds)
+- **Preprocessing**: Augmentations applied at training time (see Data Augmentation section)
+
+## Evaluation Results
+
+### Comprehensive Metrics
+All files from The model was evaluated on a held-out test set of 1,928 images 
+containing 9,825 instances across all 17 classes. The model demonstrates 
+strong performance across all metrics, achieving near-perfect precision 
+and recall with a mAP50 of 0.992. 
+
+| Metric    | Value |
+| --------- | ----- |
+| Precision | 0.989 |
+| Recall    | 0.985 |
+| mAP50     | 0.992 |
+| mAP50-95  | 0.862 |
+
+###
+