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docs: add sample image download and sv.plot_image() to detect_and_annotate tutorial #2242

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43 changes: 28 additions & 15 deletions docs/how_to/detect_and_annotate.md
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Expand Up @@ -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),
Expand All @@ -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]
```

Expand All @@ -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]
```

Expand All @@ -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():
Expand Down Expand Up @@ -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)
```
Expand All @@ -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)
```
Expand All @@ -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():
Expand Down Expand Up @@ -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)

Expand All @@ -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)

Expand All @@ -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():
Expand Down Expand Up @@ -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)

Expand All @@ -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)

Expand Down Expand Up @@ -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():
Expand Down Expand Up @@ -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)

Expand All @@ -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"
Expand All @@ -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)

Expand Down Expand Up @@ -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():
Expand Down
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