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