Grounding DINO vs. DETIC: Compared and Contrasted

Compare and DETIC with Autodistill

Using Autodistill, you can compare Grounding DINO and DETIC 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-detic

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


from autodistill_grounding_dino import GroundingDINO
from autodistill_detic import DETIC

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

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

models = [
    GroundingDINO(ontology=ontology),
    DETIC(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. DETIC

Both

and

DETIC

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

and

DETIC

		DETIC
Date of Release		Jan 07, 2022
Model Type		Instance Segmentation
Architecture
GitHub Stars		1800

Using Autodistill, you can compare Grounding DINO and DETIC 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-detic

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


from autodistill_grounding_dino import GroundingDINO
from autodistill_detic import DETIC

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

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

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

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

DETIC

Detic is an open source segmentation model developed by Meta Research and released in 2022.

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

https://blog.roboflow.com/what-is-detic/

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Grounding DINO vs. DETIC

Models

DETIC

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Grounding DINO vs. DETIC

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