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:
Next, create a new Python file and add the following code:
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:
.
Both
and
are commonly used in computer vision projects. Below, we compare and contrast
and
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:
Next, create a new Python file and add the following code:
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:
GroundedSAM combines Grounding DINO with the Segment Anything Model to identify and segment objects in an image given text captions.
How to AugmentHow to LabelHow to Plot PredictionsHow to Filter PredictionsHow to Create a Confusion MatrixFastSAM is an image segmentation model trained using 2% of the data in the Segment Anything Model SA-1B dataset.
How to AugmentHow to LabelHow to Plot PredictionsHow to Filter PredictionsHow to Create a Confusion Matrix
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