VLPart vs. OWL ViT: Compared and Contrasted

Compare VLPart and OWL ViT with Autodistill

Using Autodistill, you can compare VLPart and OWL ViT 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-vlpart autodistill-owl-vit

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


from autodistill_vlpart import VLPart
from autodistill_owl_vit import OWLViT

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

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

models = [
    VLPart(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



VLPart vs. OWL ViT

Both

VLPart

and

OWL ViT

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

VLPart

and

OWL ViT

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

Using Autodistill, you can compare VLPart and OWL ViT 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-vlpart autodistill-owl-vit

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


from autodistill_vlpart import VLPart
from autodistill_owl_vit import OWLViT

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

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

models = [
    VLPart(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"
)

‍

VLPart

VLPart, developed by Meta Research, is an object detection and segmentation model that works with an open vocabulary

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

OWL ViT

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

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

Compare VLPart to other models

Compare OWL ViT to other models

VLPart

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VLPart vs. OWL ViT

Models

VLPart

OWL ViT

Compare VLPart and OWL ViT with Autodistill

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VLPart vs. OWL ViT

VLPart

OWL ViT

Compare VLPart to other models

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