Update README.md

README.md

@@ -133,4 +133,41 @@ and recall with a mAP50 of 0.992.
 | seasoner         | 302         | 572       | 0.986     | 0.965  | 0.993 | 0.849    |
 | stationery       | 162         | 300       | 0.986     | 0.957  | 0.972 | 0.785    |
 | tissue           | 482         | 978       | 0.999     | 0.994  | 0.995 | 0.909    |
- 
+
+### Visual Examples of Classes
+
+blah blah do this later
+
+### Key Visualizations
+
+#### Confusion Matrix
+![Confusion Matrix](confusion_matrix_normalized.png)
+
+#### F1 Confidence Curve
+![F1 Curve](F1_curve.png)
+
+#### Training & Validation Loss Curves
+![Results](results.png)
+
+### Performance Analysis
+
+The model performs consistently well across all 17 classes on the validation 
+dataset, with the lowest mAP50 being **stationery** at 0.972. The strongest 
+performing classes were **tissue** and **puffed_food** (mAP50-95: 0.909, 0.907), 
+likely due to their distinct packaging shapes and high training sample 
+counts. The weakest performing class was **stationery** 
+(mAP50: 0.972, mAP50-95: 0.785), which is also the 
+smallest class at 1,466 training images, suggesting performance 
+is partially limited by sample size.
+
+## Limitations and Biases
+
+When tested on the **D2S dataset** (wild images), 
+performance dropped significantly. The model missed entire 
+objects, produced low-confidence detections, and misclassified items.
+For example, it labeled a water bottle as `instant_noodles`. This 
+suggests the model may have overfit to the specific visual patterns 
+of the training data, or alternatively reflects a domain gap between 
+Asian grocery packaging (training data) and the European products in D2S. 
+Both explanations are plausible and further testing on diverse datasets 
+would be needed to distinguish between them.