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docs/how_to/detect_and_annotate.md
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@ -4,17 +4,17 @@ comments: true
# Detect and Annotate
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),
[Ultralytics](https://github.com/ultralytics/ultralytics) or
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),
[Ultralytics](https://github.com/ultralytics/ultralytics) or
[Transformers](https://github.com/huggingface/transformers) packages. Following this,
you'll learn how to import these predictions into Supervision and use them to annotate
you'll learn how to import these predictions into Supervision and use them to annotate
source image.
## Run Inference
First, you'll need to obtain predictions from your object detection or segmentation
First, you'll need to obtain predictions from your object detection or segmentation
model.
=== "Inference"
@ -51,10 +51,10 @@ model.
image = Image.open(<PATH TO IMAGE>)
inputs = processor(images=image, return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)
width, height = image.size
target_size = torch.tensor([[height, width]])
results = processor.post_process_object_detection(
@ -110,10 +110,10 @@ Now that we have predictions from a model, we can load them into Supervision.
image = Image.open(<PATH TO IMAGE>)
inputs = processor(images=image, return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)
width, height = image.size
target_size = torch.tensor([[height, width]])
results = processor.post_process_object_detection(
@ -188,16 +188,16 @@ Finally, we can annotate the image with the predictions. Since we are working wi
image = Image.open(<PATH TO IMAGE>)
inputs = processor(images=image, return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)
width, height = image.size
target_size = torch.tensor([[height, width]])
results = processor.post_process_object_detection(
outputs=outputs, target_sizes=target_size)[0]
detections = sv.Detections.from_transformers(results)
bounding_box_annotator = sv.BoundingBoxAnnotator()
label_annotator = sv.LabelAnnotator()
@ -271,4 +271,4 @@ Finally, we can annotate the image with the predictions. Since we are working wi
## Annotate Image with Segmentations
If you are running the segmentation model [`sv.MaskAnnotator`](annotators/#supervision.annotators.core.MaskAnnotator) is a drop-in replacement for [`sv.BoundingBoxAnnotator`](annotators/#supervision.annotators.core.BoundingBoxAnnotator) that will allow you to draw masks instead of boxes.
If you are running the segmentation model [`sv.MaskAnnotator`](annotators/#supervision.annotators.core.MaskAnnotator) is a drop-in replacement for [`sv.BoundingBoxAnnotator`](annotators/#supervision.annotators.core.BoundingBoxAnnotator) that will allow you to draw masks instead of boxes.