First, install the supervision pip package:
Once you have installed supervision, you are ready to load your data and start writing logic to filter detections.
First, we are going to load our dataset into a supervision.DetectionDataset() object. This object will contain information about all the images in a dataset. You can load datasets from many different model types, from YOLO to MMDetection. For this guide, we will use the
DeepSparse Object Detection
data loader.
You can load data using the following code:
Replace the ... with the response object from your model.
Supervision has two annotators that let you visualize detections from a computer vision model:
1. BoxAnnotator, to plot bounding boxes.
2. MaskAnnotator, to plot segmentation masks.
You can use the following code to plot bounding boxes:
To plot segmentation masks, replace BoundingBoxAnnotator() with MaskAnnotator(). This will use the segmentation mask annotator for visualizing detections.
You can preview what different annotators look like using the following interactive widget:
supervision provides an extensive range of functionalities for working with computer vision models. With supervision, you can:
1. Process and filter detections and segmentation masks from a range of popular models (YOLOv5, Ultralytics YOLOv8, MMDetection, and more).
2. Process and filter classifications.
3. Compute confusion matrices.
And more! To learn about the full range of functionality in supervision, check out the supervision documentation.