Models
Grounding DINO vs. OWL ViT

Grounding DINO vs. OWL ViT

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

Models

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OWL ViT

OWL-ViT is a transformer-based object detection model developed by Google Research.
Model Type
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Object Detection
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Model Features
Item 1 Info
Item 2 Info
Architecture
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Frameworks
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Annotation Format
Instance Segmentation
Instance Segmentation
GitHub Stars
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Training Notebook
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Compare and OWL ViT with Autodistill

Using Autodistill, you can compare Grounding DINO and OWLViT 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-owl-vit

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


from autodistill_grounding_dino import GroundingDINO
from autodistill_owl_vit import OWLViT

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

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

models = [
    GroundingDINO(ontology=ontology),
    OWLViT(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. OWL ViT

.

Both

and

OWL ViT

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

and

OWL ViT
  OWL ViT
Date of Release
Model Type Object Detection
Architecture
GitHub Stars

Using Autodistill, you can compare Grounding DINO and OWLViT 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-owl-vit

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


from autodistill_grounding_dino import GroundingDINO
from autodistill_owl_vit import OWLViT

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

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

models = [
    GroundingDINO(ontology=ontology),
    OWLViT(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"
)

Compare to other models

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Compare OWL ViT to other models

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