Models

Grounded SAM vs. FastSAM

Both

Grounded SAM

and

FastSAM

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

Grounded SAM

and

FastSAM

.

  Grounded SAM FastSAM
Date of Release
Model Type Instance Segmentation Instance Segmentation
Architecture
GitHub Stars 3900

Compare Grounded SAM and FastSAM with Autodistill

Using Autodistill, you can compare Grounded SAM and FastSAM 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-grounded-sam autodistill-fastsam

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


from autodistill_grounded_sam import GroundedSAM
from autodistill_fastsam import FastSAM

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

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

models = [
    GroundedSAM(ontology=ontology),
    FastSAM(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 Grounded SAM to other models

Compare FastSAM to other models

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