Models
OWL ViT vs. OWLv2

OWL ViT vs. OWLv2

Both OWL ViT and OWLv2 are commonly used in computer vision projects. Below, we compare and contrast OWL ViT and OWLv2.

Models

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OWL ViT

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

OWLv2 is a transformer-based object detection model developed by Google Research. OWLv2 is the successor to OWL ViT.
Model Type
Object Detection
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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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License
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Training Notebook
Compare Alternatives

Compare OWL ViT and OWLv2 with Autodistill

Using Autodistill, you can compare OWL ViT 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-owl-vit autodistill-owlv2

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


from autodistill_owl_vit import OWLViT
from autodistill_owlv2 import OWLv2

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

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

models = [
    GroundingDINO(ontology=ontology),
    OWLv2(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

OWL ViT vs. OWLv2

.

Both

OWL ViT

and

OWLv2

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

OWL ViT

and

OWLv2
  OWL ViT OWLv2
Date of Release
Model Type Object Detection Object Detection
Architecture
GitHub Stars

Using Autodistill, you can compare OWL ViT 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-owl-vit autodistill-owlv2

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


from autodistill_owl_vit import OWLViT
from autodistill_owlv2 import OWLv2

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

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

models = [
    GroundingDINO(ontology=ontology),
    OWLv2(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"
)

OWL ViT

OWL-ViT is a transformer-based object detection model developed by Google Research.

How to AugmentHow to LabelHow to Plot PredictionsHow to Filter PredictionsHow 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 AugmentHow to LabelHow to Plot PredictionsHow to Filter PredictionsHow to Create a Confusion Matrix

Compare OWL ViT to other models

Compare OWLv2 to other models

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