Update README.md

examples/README.md

@@ -5,6 +5,6 @@ This folder contains various examples of Valor usage.
 | File | Description |
 | --- | --- |
 | [getting_started.ipynb](getting_started.ipynb) | A Jupyter notebook that walks through the basics of using Valor. ***This is a good place to start!*** |
-| [pedestrian_detection.ipynb](pedestrian-detection.ipynb) | A Jupyter notebook that walks through an object detection example, showing how to use the power of Valor's filtering functionality to provide a fine-grained analysis of model performance with respect to user defined business logic. ***This is a good place to go after `getting_started.ipynb`*** |
+| [pedestrian_detection.ipynb](pedestrian_detection.ipynb) | A Jupyter notebook that walks through an object detection example, showing how to use the power of Valor's filtering functionality to provide a fine-grained analysis of model performance with respect to user defined business logic. ***This is a good place to go after `getting_started.ipynb`*** |
 | [tabular_classification.ipynb](tabular_classification.ipynb) | A Jupyter notebook showing an end-to-end example of evaluating a scikit-learn classification model. |
-| [detection](detection) | This folder demonstrates both how to evaluate an object detection model and provides example scripts of how to integrate models and datasets into Valor. `integrations/coco_integration.py` demonstrates (using the COCO dataset as an example) the type of integration code necessary to integrate existing annotations into Valor, while `yolo_integration.py` demonstrates (using the Ultralytics YOLO model as an example) the type of integration code necessary to integrate model outputs into Valor. The notebook `coco-yolo.ipynb` shows, using the integration scripts, how to evaluate an object detection model.  |
+| [detection](detection) | This folder demonstrates both how to evaluate an object detection model and provides example scripts of how to integrate models and datasets into Valor. `integrations/coco_integration.py` demonstrates (using the COCO dataset as an example) the type of integration code necessary to integrate existing annotations into Valor, while `yolo_integration.py` demonstrates (using the Ultralytics YOLO model as an example) the type of integration code necessary to integrate model outputs into Valor. The notebook `coco-yolo.ipynb` shows, using the integration scripts, how to evaluate an object detection model.  |