The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.
I'm seeking a method to fine-tune SAM for generating custom segments. Below, you'll find two segmented images of a chair. I've employed a specific logic for segmentation (where each color represents a segment).
Segmented Image 1:
Segmented Image 2:
I went through a lot of SAM fine-tuning procedures, but all of them use binary or multiclass masks. When it comes to fine-tuning for segmentation, there is no straightforward method for defining labels for the masks. This is because it's possible that the ground truth contains 5 masks while the predicted output contains 10 masks. Consequently, it becomes challenging to compare them and optimize the process.
My segmentation approach involves the transformation of a 3D chair into a 2D representation. Each segmented part can be isolated and then unwrapped into a 2D format. I am segmenting the 3D chair in a manner that allows each segmented part to be presented as a 2D representation.
szym1998
commented
6 months ago
I'm seeking a method to fine-tune SAM for generating custom segments. Below, you'll find two segmented images of a chair. I've employed a specific logic for segmentation (where each color represents a segment).
Segmented Image 1:
Segmented Image 2:
I went through a lot of SAM fine-tuning procedures, but all of them use binary or multiclass masks. When it comes to fine-tuning for segmentation, there is no straightforward method for defining labels for the masks. This is because it's possible that the ground truth contains 5 masks while the predicted output contains 10 masks. Consequently, it becomes challenging to compare them and optimize the process.
My segmentation approach involves the transformation of a 3D chair into a 2D representation. Each segmented part can be isolated and then unwrapped into a 2D format. I am segmenting the 3D chair in a manner that allows each segmented part to be presented as a 2D representation.