Grounding DINO vs. OWLv2: Compared and Contrasted

Compare GroundingDINO and OWLv2 with Autodistill

Using Autodistill, you can compare Grounding DINO and OWLv2 on your own images in a few lines of code.

Here is an example comparison:

To start a comparison, first install the required dependencies:


pip install autodistill autodistill-grounding-dino autodistill-owlv2

Next, create a new Python file and add the following code:


from autodistill_grounding_dino import GroundingDINO
from autodistill_detic import DETIC

from autodistill.detection import CaptionOntology
from autodistill.utils import compare

ontology = CaptionOntology(
    {
        "solar panel": "solar panel",
    }
)

models = [
    GroundingDINO(ontology=ontology),
    DETIC(ontology=ontology)
]

images = [
    "/home/user/autodistill/solarpanel1.jpg",
    "/home/user/autodistill/solarpanel2.jpg"
]

compare(
    models=models,
    images=images
)

Above, replace the images in the `images` directory with the images you want to use.

The images must be absolute paths.

Then, run the script.

You should see a model comparison like this:

When you have chosen a model that works best for your use case, you can auto label a folder of images using the following code:


base_model.label(
  input_folder="./images",
  output_folder="./dataset",
  extension=".jpg"
)

‍

Models



Grounding DINO vs. OWLv2

Both

GroundingDINO

and

OWLv2

are commonly used in computer vision projects. Below, we compare and contrast

GroundingDINO

and

OWLv2

	GroundingDINO	OWLv2
Date of Release
Model Type	Object Detection	Object Detection
Architecture
GitHub Stars	4600

Using Autodistill, you can compare Grounding DINO and OWLv2 on your own images in a few lines of code.

Here is an example comparison:

To start a comparison, first install the required dependencies:


pip install autodistill autodistill-grounding-dino autodistill-owlv2

Next, create a new Python file and add the following code:


from autodistill_grounding_dino import GroundingDINO
from autodistill_detic import DETIC

from autodistill.detection import CaptionOntology
from autodistill.utils import compare

ontology = CaptionOntology(
    {
        "solar panel": "solar panel",
    }
)

models = [
    GroundingDINO(ontology=ontology),
    DETIC(ontology=ontology)
]

images = [
    "/home/user/autodistill/solarpanel1.jpg",
    "/home/user/autodistill/solarpanel2.jpg"
]

compare(
    models=models,
    images=images
)

Above, replace the images in the `images` directory with the images you want to use.

The images must be absolute paths.

Then, run the script.

You should see a model comparison like this:

When you have chosen a model that works best for your use case, you can auto label a folder of images using the following code:


base_model.label(
  input_folder="./images",
  output_folder="./dataset",
  extension=".jpg"
)

‍

GroundingDINO

Grounding DINO is a zero-shot object detection model made by combining a Transformer-based DINO detector and grounded pre-training.

How to Augment How to Label How to Plot Predictions How to Filter Predictions How to Create a Confusion Matrix

OWLv2

OWLv2 is a transformer-based object detection model developed by Google Research. OWLv2 is the successor to OWL ViT.

How to Augment How to Label How to Plot Predictions How to Filter Predictions How to Create a Confusion Matrix

Compare GroundingDINO to other models

OWLv2

Compare OWLv2 to other models

VLPart

OWL ViT

GroundingDINO

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Grounding DINO vs. OWLv2

Models

GroundingDINO

OWLv2

Compare GroundingDINO and OWLv2 with Autodistill

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Grounding DINO vs. OWLv2

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