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Quantifying SAM's performance on multi-class segmentation using clustering consensus metrics

Poses the problem of quantifying SAM's zero-shot performance on multiclass segmentation as a clustering consensus problem.

Paper: https://arxiv.org/pdf/2311.15138.pdf

Setup

  1. Get the codebase of SAM - git clone https://github.com/facebookresearch/segment-anything
  2. Get this codebase and save it in the top-level directory of SAM - cd segment-anything then git clone https://github.com/madlab-ucr/sam4crops.git
  3. Download SAM weights from step 1 repo github page and store them in segment-anything/sam4crops/cached_models

[Dataset] https://drive.google.com/drive/folders/1EnXXRHNoTyIbM-_5p-P9pH4zH3xyTqBp?usp=drive_link

Codebase

  1. src: Folder containing scripts

    • GettingStarted.ipynb: My one-stop notebook for a brief EDA and prediction visualization.
    • make_aoi_samples.py: Script to make samples for experiments from the CalCrop21 benchmark. Step 1 of 3.
    • grid_search.py: Script for grid search over all experimental parameters. Step 2 of 3.
    • ResultsViz.ipynb: Notebook to visualize results of grid search. Step 3 of 3.
    • utils.py: Useful plotting and other utils.
    • unsuable_tiles.txt: This are the tiles from Calcrop21 that are deemed not suitable for this analysis after the max NDVI RGB extraction.
    • colormap.py: A colormap for the CDL.

  2. cached_models: Folder to save SAM weights

  3. results: Folder to store results