This is a simplified guide to an AI model called Sks maintained by Simbrams. If you like these kinds of analysis, you should join AImodels.fyi or follow us on Twitter.
Model overview
The sks model, created by simbrams, is a C++ implementation of a sky segmentation model that can accurately segment skies in outdoor images. This model is built using the U-2-Net architecture, which has proven effective for sky segmentation tasks. While the model does not include the "Density Estimation" feature mentioned in the original paper, it still provides high-quality sky masks that can be further refined through post-processing.
Model inputs and outputs
The sks model takes an image as input and outputs a segmented sky mask. The input image can be resized and contrast adjusted to optimize the model's performance. Additionally, the model can be configured to keep the inference engine alive for faster subsequent inferences.
Inputs
- Image: The input image for sky segmentation.
- Contrast: An integer value to adjust the contrast of the input image, with a default of 100.
- Keep Alive: A boolean flag to keep the model's inference engine alive, with a default of false.
Outputs
- Segmented Sky Mask: An array of URI strings representing the segmented sky regions in the input image.
Capabilities
The sks model demonstrates strong sk...
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