Models
Grounding DINO vs. DETIC

Grounding DINO vs. DETIC

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

Models

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DETIC

Detic is an open source segmentation model developed by Meta Research and released in 2022.
Model Type
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Instance Segmentation
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Model Features
Item 1 Info
Item 2 Info
Architecture
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Frameworks
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PyTorch
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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 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 1400

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"
)

Compare to other models

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Compare DETIC to other models

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