Models
Grounding DINO vs. Kosmos-2

Grounding DINO vs. Kosmos-2

Both and Kosmos-2 are commonly used in computer vision projects. Below, we compare and contrast and Kosmos-2.

Models

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Kosmos-2

Kosmos-2 is a multimodal language model capable of object detection and grounding text in images.
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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License
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Training Notebook
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Compare and Kosmos-2 with Autodistill

Using Autodistill, you can compare Grounding DINO and Kosmos-2 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-kosmos-2

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


from autodistill_grounding_dino import GroundingDINO
from autodistill_kosmos_2 import Kosmos2

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

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

models = [
    GroundingDINO(ontology=ontology),
    Kosmos2(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. Kosmos-2

.

Both

and

Kosmos-2

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

and

Kosmos-2
  Kosmos-2
Date of Release
Model Type Object Detection
Architecture
GitHub Stars

Using Autodistill, you can compare Grounding DINO and Kosmos-2 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-kosmos-2

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


from autodistill_grounding_dino import GroundingDINO
from autodistill_kosmos_2 import Kosmos2

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

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

models = [
    GroundingDINO(ontology=ontology),
    Kosmos2(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"
)

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