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

icon-model

DETIC

Detic is an open source segmentation model developed by Meta Research and released in 2022.
Model Type
--
Instance Segmentation
--
Model Features
Item 1 Info
Item 2 Info
Architecture
--
--
Frameworks
--
PyTorch
--
Annotation Format
Instance Segmentation
Instance Segmentation
GitHub Stars
--
1.8k+
--
License
--
Apache 2.0
--
Training Notebook
Compare Alternatives
--
Compare with...

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"
)