Models
OWLv2 vs. CoDet

OWLv2 vs. CoDet

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

Models

icon-model

OWLv2

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

CoDet

CoDet is an open vocabulary zero-shot object detection model.
Learn more about CoDet
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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79
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License
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Apache 2.0 License
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Training Notebook
Compare Alternatives
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Compare OWLv2 and CoDet with Autodistill

Using Autodistill, you can compare OWLv2 and CoDet 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-owlv2 autodistill-codet

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


from autodistill_owlv2 import OWLv2
from autodistill_codet import CoDet

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

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

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

OWLv2 vs. CoDet

.

Both

OWLv2

and

CoDet

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

OWLv2

and

CoDet
  OWLv2 CoDet
Date of Release Oct 24, 2023
Model Type Object Detection Object Detection
Architecture
GitHub Stars 79

Using Autodistill, you can compare OWLv2 and CoDet 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-owlv2 autodistill-codet

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


from autodistill_owlv2 import OWLv2
from autodistill_codet import CoDet

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

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

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

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 OWLv2 to other models

Compare CoDet to other models

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