SAM-CLIP vs. FastSAM: Compared and Contrasted

Compare SAM-CLIP and FastSAM with Autodistill

Using Autodistill, you can compare SAM-CLIP 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-fastsam autodistill-sam-clip

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


from autodistill_fastsam import FastSAM
from autodistill_sam_clip import SAMCLIP

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

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

models = [
    FastSAM(ontology=ontology),
    SAMCLIP(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



SAM-CLIP vs. FastSAM

Both

SAM-CLIP

and

FastSAM

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

SAM-CLIP

and

FastSAM

	SAM-CLIP	FastSAM
Date of Release
Model Type	Instance Segmentation	Instance Segmentation
Architecture	Combination of Segment Anything and CLIP
GitHub Stars	20	6700

Using Autodistill, you can compare SAM-CLIP 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-fastsam autodistill-sam-clip

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


from autodistill_fastsam import FastSAM
from autodistill_sam_clip import SAMCLIP

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

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

models = [
    FastSAM(ontology=ontology),
    SAMCLIP(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"
)

SAM-CLIP

Use Grounding DINO, Segment Anything, and CLIP to label objects in images.

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

FastSAM

FastSAM is an image segmentation model trained using 2% of the data in the Segment Anything Model SA-1B dataset.

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

Learn more about FastSAM

Compare SAM-CLIP to other models

FastSAM

Compare FastSAM to other models

SAM-CLIP

Grounded SAM

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SAM-CLIP vs. FastSAM

Models

SAM-CLIP

FastSAM

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SAM-CLIP vs. FastSAM

SAM-CLIP

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