OwlV2 (Object Detection)

OWLv2 is an open set object detectio model trained by Google. OWLv2 was primarily trained to detect objects from text. The implementation in Inference currently only supports detecting objects from visual examples of that object.

Installation¶

To install inference with the extra dependencies necessary to run OWLv2, run

pip install inference[transformers]

or

pip install inference-gpu[transformers]

How to Use OWLv2¶

Create a new Python file called app.py and add the following code:

import inference
from inference.models.owlv2.owlv2 import OwlV2
from inference.core.entities.requests.owlv2 import OwlV2InferenceRequest
from PIL import Image
import io
import base64

model = OwlV2()


im_url = "https://media.roboflow.com/inference/seawithdock.jpeg"
image = {
    "type": "url",
    "value": im_url
}
request = OwlV2InferenceRequest(
    image=image,
    training_data=[
        {
            "image": image,
            "boxes": [{"x": 223, "y": 306, "w": 40, "h": 226, "cls": "post"}],
        }
    ],
    visualize_predictions=True,
    confidence=0.9999,
)

response = OwlV2().infer_from_request(request)

def load_image_from_base64(base64_str):
    image = Image.open(io.BytesIO(base64_str))
    return image

visualization = load_image_from_base64(response.visualization)
visualization.save("owlv2_visualization.jpg")

In this code, we run OWLv2 on an image, using example objects from that image. Above, replace:

1. training_data with the locations of the objects you want to detect.
2. im_url with the image you would like to perform inference on.

Then, run the Python script you have created:

python app.py

The result from your model will be save to disk at owlv2_visualization.jpg

Note the blue bounding boxes surrounding each pole of the dock.