Update README.md

README.md

@@ -168,3 +168,40 @@ The training results plots also show that loss decreases and evaluation metrics
 Overall, the model works well for clear package images, but performance may decrease when packages are partially occluded, poorly lit, or when damage is subtle.
 
 ## 5. Limitations and Biases
+
+### Known Failure Cases
+
+![Failure Case](failurecase.png)
+
+- The image above shows an example where the model predicts Damaged with a confidence of 0.85.
+- The box shows minor deformation, but the damage is relatively subtle and could be interpreted differently by a human observer.
+- The model appears to rely on visual cues such as bent edges, creases in the cardboard, or irregular box shapes when identifying damage.
+- Because damaged packages in the training data often contain stronger visual damage signals, the model may sometimes overpredict the damaged class when it encounters boxes with minor wear or shipping creases.
+
+### Poor Performing Classes
+
+- The normal class performs slightly worse than the damaged class.
+- Damaged packages often contain clearer visual features such as dents, crushed corners, or tears, which makes them easier for the model to recognize.
+- Normal boxes can sometimes look visually similar to slightly damaged boxes or to background regions.
+
+### Data Biases
+
+- The dataset mainly contains cardboard shipping boxes, which limits how well the model can generalize to other package types.
+- Other packaging formats such as padded envelopes, plastic mailers, or irregular parcels are not represented in the training data.
+- Many images were collected in relatively controlled conditions, which may differ from real-world delivery environments.
+
+### Environmental and Contextual Limitations
+
+- Model performance may decrease when lighting conditions are poor or when strong shadows are present.
+- Packages that blend into the surrounding background may be harder for the model to detect.
+- Detection may also become more difficult when packages are partially occluded or appear very small in the image.
+
+### Inappropriate Use Cases
+
+- This model should not be used as a fully automated system for determining package damage in real-world logistics environments.
+- Predictions should be reviewed by a human operator, especially when model confidence is low or when the damage is subtle.
+
+### Sample Size Limitations
+
+- The dataset used for training contains approximately 2,787 images across two classes.
+- A larger and more diverse dataset would likely improve the model’s ability to generalize and reduce detection errors.