Universe Models¶
Roboflow Universe is a public catalog of 50,000+ computer vision models published by other Roboflow users - covering everything from defect detection and sports analytics to wildlife identification. This page shows how to pick one of those community models and run it with Inference.
Run a Model From Roboflow Universe¶
Let's pick a community model from Universe and run it.
Go to the Roboflow Universe homepage and use the search bar to find a model.
Info
Add "model" to your search query to only find models.
Browse the search page to find a model.
When you have found a model, click on the model card to learn more. Click the "Model" link in the sidebar to get the information you need to use the model.
Next, create a new Python file and add the following code. See Run a Model for package installation (including GPU and HTTP-client variants) and Supervision for the visualization helpers used below.
# import the HTTP client for sending inference requests to an Inference Server
from inference_sdk import InferenceHTTPClient
# import supervision to visualize our results
import supervision as sv
# import cv2 to help load our image
import cv2
# define the image url to use for inference
image_file = "people-walking.jpg"
image = cv2.imread(image_file)
# connect to an Inference Server (Roboflow-hosted or self-hosted)
client = InferenceHTTPClient(
# api_url="http://localhost:9001", # for Self-hosted
api_url="https://serverless.roboflow.com",
api_key="ROBOFLOW_API_KEY",
)
# Run the inference with cup-detection-cevbw/1 (Yolov8s) model from Universe
results = client.infer(image, model_id="cup-detection-cevbw/1")
# load the results into the supervision Detections api
detections = sv.Detections.from_inference(results)
# create supervision annotators
bounding_box_annotator = sv.BoxAnnotator()
label_annotator = sv.LabelAnnotator()
# annotate the image with our inference results
annotated_image = bounding_box_annotator.annotate(
scene=image, detections=detections)
annotated_image = label_annotator.annotate(
scene=annotated_image, detections=detections)
# display the image
sv.plot_image(annotated_image)
# import a utility function for loading Roboflow models
from inference import get_model
# import supervision to visualize our results
import supervision as sv
# import cv2 to help load our image
import cv2
# define the image url to use for inference
image_file = "people-walking.jpg"
image = cv2.imread(image_file)
# load the cup-detection-cevbw/1 (Yolov8s) from Universe
model = get_model(model_id="cup-detection-cevbw/1")
# run inference on our chosen image, image can be a url, a numpy array, a PIL image, etc.
results = model.infer(image)
# load the results into the supervision Detections api
detections = sv.Detections.from_inference(results[0].dict(by_alias=True, exclude_none=True))
# create supervision annotators
bounding_box_annotator = sv.BoxAnnotator()
label_annotator = sv.LabelAnnotator()
# annotate the image with our inference results
annotated_image = bounding_box_annotator.annotate(
scene=image, detections=detections)
annotated_image = label_annotator.annotate(
scene=annotated_image, detections=detections)
# display the image
sv.plot_image(annotated_image)
Tip
To see more models, check out the Pre-Trained Models page and Roboflow Universe.
The people-walking.jpg file is hosted here.
Replace rfdetr-small with the model ID you found on Universe, replace image with the image of your choosing, and be sure to export your API key:
export ROBOFLOW_API_KEY=<your api key>
Then, run the Python script:
python app.py
You should see your model's predictions visualized on your screen.
