diff --git a/docs/how_to/detect_and_annotate.md b/docs/how_to/detect_and_annotate.md
index a9bf701ac9..e919fd8669 100644
--- a/docs/how_to/detect_and_annotate.md
+++ b/docs/how_to/detect_and_annotate.md
@@ -13,6 +13,18 @@ date_modified: 2026-04-22
 
 # Detect and Annotate
 
+!!! tip "Sample Image"
+
+    Don't have an image? Download the one used in this tutorial:
+
+```bash
+wget https://media.roboflow.com/notebooks/examples/dog.jpeg
+```
+
+```
+Then replace `<SOURCE_IMAGE_PATH>` with `"dog.jpeg"`.
+```
+
 Supervision provides a seamless process for annotating predictions generated by various
 object detection and segmentation models. This guide shows how to perform inference
 with the [Inference](https://github.com/roboflow/inference),
@@ -37,7 +49,7 @@ To run inference, initialize your chosen model and pass the source image to its
     from inference import get_model
 
     model = get_model(model_id="yolov8n-640")
-    image = cv2.imread("<SOURCE_IMAGE_PATH>")
+    image = cv2.imread("dog.jpeg")
     results = model.infer(image)[0]
     ```
 
@@ -48,7 +60,7 @@ To run inference, initialize your chosen model and pass the source image to its
     from ultralytics import YOLO
 
     model = YOLO("yolov8n.pt")
-    image = cv2.imread("<SOURCE_IMAGE_PATH>")
+    image = cv2.imread("dog.jpeg")
     results = model(image)[0]
     ```
 
@@ -62,7 +74,7 @@ To run inference, initialize your chosen model and pass the source image to its
     processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50")
     model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50")
 
-    image = Image.open("<SOURCE_IMAGE_PATH>")
+    image = Image.open("dog.jpeg")
     inputs = processor(images=image, return_tensors="pt")
 
     with torch.no_grad():
@@ -91,7 +103,7 @@ Each supported framework has a dedicated class method on `sv.Detections` that co
     from inference import get_model
 
     model = get_model(model_id="yolov8n-640")
-    image = cv2.imread("<SOURCE_IMAGE_PATH>")
+    image = cv2.imread("dog.jpeg")
     results = model.infer(image)[0]
     detections = sv.Detections.from_inference(results)
     ```
@@ -106,7 +118,7 @@ Each supported framework has a dedicated class method on `sv.Detections` that co
     from ultralytics import YOLO
 
     model = YOLO("yolov8n.pt")
-    image = cv2.imread("<SOURCE_IMAGE_PATH>")
+    image = cv2.imread("dog.jpeg")
     results = model(image)[0]
     detections = sv.Detections.from_ultralytics(results)
     ```
@@ -124,7 +136,7 @@ Each supported framework has a dedicated class method on `sv.Detections` that co
     processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50")
     model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50")
 
-    image = Image.open("<SOURCE_IMAGE_PATH>")
+    image = Image.open("dog.jpeg")
     inputs = processor(images=image, return_tensors="pt")
 
     with torch.no_grad():
@@ -161,7 +173,7 @@ To draw bounding boxes and class labels on your image, create a `BoxAnnotator` a
     from inference import get_model
 
     model = get_model(model_id="yolov8n-640")
-    image = cv2.imread("<SOURCE_IMAGE_PATH>")
+    image = cv2.imread("dog.jpeg")
     results = model.infer(image)[0]
     detections = sv.Detections.from_inference(results)
 
@@ -182,7 +194,7 @@ To draw bounding boxes and class labels on your image, create a `BoxAnnotator` a
     from ultralytics import YOLO
 
     model = YOLO("yolov8n.pt")
-    image = cv2.imread("<SOURCE_IMAGE_PATH>")
+    image = cv2.imread("dog.jpeg")
     results = model(image)[0]
     detections = sv.Detections.from_ultralytics(results)
 
@@ -206,7 +218,7 @@ To draw bounding boxes and class labels on your image, create a `BoxAnnotator` a
     processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50")
     model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50")
 
-    image = Image.open("<SOURCE_IMAGE_PATH>")
+    image = Image.open("dog.jpeg")
     inputs = processor(images=image, return_tensors="pt")
 
     with torch.no_grad():
@@ -245,7 +257,7 @@ override this behavior by passing a list of custom `labels` to the `annotate` me
     from inference import get_model
 
     model = get_model(model_id="yolov8n-640")
-    image = cv2.imread("<SOURCE_IMAGE_PATH>")
+    image = cv2.imread("dog.jpeg")
     results = model.infer(image)[0]
     detections = sv.Detections.from_inference(results)
 
@@ -272,7 +284,7 @@ override this behavior by passing a list of custom `labels` to the `annotate` me
     from ultralytics import YOLO
 
     model = YOLO("yolov8n.pt")
-    image = cv2.imread("<SOURCE_IMAGE_PATH>")
+    image = cv2.imread("dog.jpeg")
     results = model(image)[0]
     detections = sv.Detections.from_ultralytics(results)
 
@@ -302,7 +314,7 @@ override this behavior by passing a list of custom `labels` to the `annotate` me
     processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50")
     model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50")
 
-    image = Image.open("<SOURCE_IMAGE_PATH>")
+    image = Image.open("dog.jpeg")
     inputs = processor(images=image, return_tensors="pt")
 
     with torch.no_grad():
@@ -349,7 +361,7 @@ that will allow you to draw masks instead of boxes.
     from inference import get_model
 
     model = get_model(model_id="yolov8n-seg-640")
-    image = cv2.imread("<SOURCE_IMAGE_PATH>")
+    image = cv2.imread("dog.jpeg")
     results = model.infer(image)[0]
     detections = sv.Detections.from_inference(results)
 
@@ -364,6 +376,7 @@ that will allow you to draw masks instead of boxes.
         scene=annotated_image,
         detections=detections,
     )
+    sv.plot_image(annotated_image)
     ```
 
 === "Ultralytics"
@@ -374,7 +387,7 @@ that will allow you to draw masks instead of boxes.
     from ultralytics import YOLO
 
     model = YOLO("yolov8n-seg.pt")
-    image = cv2.imread("<SOURCE_IMAGE_PATH>")
+    image = cv2.imread("dog.jpeg")
     results = model(image)[0]
     detections = sv.Detections.from_ultralytics(results)
 
@@ -402,7 +415,7 @@ that will allow you to draw masks instead of boxes.
     processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50-panoptic")
     model = DetrForSegmentation.from_pretrained("facebook/detr-resnet-50-panoptic")
 
-    image = Image.open("<SOURCE_IMAGE_PATH>")
+    image = Image.open("dog.jpeg")
     inputs = processor(images=image, return_tensors="pt")
 
     with torch.no_grad():