Models
Grounding DINO vs. CoDet

Grounding DINO vs. CoDet

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

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CoDet

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

Using Autodistill, you can compare Grounding DINO 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-grounding-dino autodistill-codet

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


from autodistill_grounding_dino import GroundingDINO
from autodistill_codet import CoDet

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

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

models = [
    GroundingDINO(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

Grounding DINO vs. CoDet

.

Both

and

CoDet

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

and

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

Using Autodistill, you can compare Grounding DINO 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-grounding-dino autodistill-codet

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


from autodistill_grounding_dino import GroundingDINO
from autodistill_codet import CoDet

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

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

models = [
    GroundingDINO(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"
)

Compare to other models

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