how to set the parameters for sam2 for sentinel 2 visual bands?

[automataIA]

How to set the parameters for sam2 for sentinel 2 visual bands?
Parameters:

suggested_params_dict = {
    'model_id': 'sam2-hiera-large',
    'device': 'cuda',
    'empty_cache': True,
    'automatic': True,
    'video': False,
    'mode': 'eval',
    'hydra_overrides_extra': None,
    'apply_postprocessing': True,
    'points_per_side': 16,  
    'points_per_batch': 8,
    'pred_iou_thresh': 0.5,
    'stability_score_thresh': 0.5,
    'stability_score_offset': 0.1,
    'mask_threshold': 0.5,
    'box_nms_thresh': 0.3,
    'crop_n_layers': 2,     
    'crop_nms_thresh': 0.3,
    'crop_overlap_ratio': 0.5,
    'crop_n_points_downscale_factor': 2,
    'point_grids': None,
    'min_mask_region_area': 50, 
    'output_mode': "binary_mask",
    'use_m2m': True,
    'multimask_output': True,
    'max_hole_area': 20,     
    'max_sprinkle_area': 20   
}

context:

• 1 pixel = 10 meter
• many small objects to detect.
  

[khlaifiabilel]

For a conventional 13-band Sentinel-2 stack, first make sure SAM receives a true-color 10 m composite rather than the first three stored bands. In the current SamGeo2.generate, bands uses 1-based indices, so use bands=[4, 3, 2] only if your file follows the standard B01...B12 ordering. Confirm the actual raster band order first.

Your proposed quality thresholds (pred_iou_thresh=0.5, stability_score_thresh=0.5) are permissive and will retain many weak masks. I would start near the current defaults and change one parameter at a time:

from samgeo.samgeo2 import SamGeo2

sam = SamGeo2(
    model_id="sam2-hiera-large",
    device="cuda",
    automatic=True,
    apply_postprocessing=True,
    points_per_side=64,
    points_per_batch=8,
    pred_iou_thresh=0.80,
    stability_score_thresh=0.92,
    stability_score_offset=1.0,
    mask_threshold=0.0,
    box_nms_thresh=0.7,
    crop_n_layers=1,
    crop_nms_thresh=0.7,
    crop_overlap_ratio=0.5,
    crop_n_points_downscale_factor=2,
    min_mask_region_area=25,
    output_mode="binary_mask",
    use_m2m=False,
    multimask_output=False,
)

sam.generate(
    "sentinel2.tif",
    output="objects.tif",
    bands=[4, 3, 2],
    unique=True,
)

At 10 m resolution, one pixel represents 100 m², so min_mask_region_area=25 removes regions below roughly 2,500 m². Set it from the smallest real object you need rather than treating 50 as universal.

For smaller objects, tune in this order:

1. Increase points_per_side from 32 to 64; try 96 or 128 only if recall remains poor. Sampling cost grows approximately with its square.
2. Use crop_n_layers=1. Additional crop layers can recover small objects but increase runtime and duplicate masks.
3. Keep points_per_batch as a memory/throughput control; reducing it helps GPU memory but does not increase sampling density.
4. Lower pred_iou_thresh or stability_score_thresh only after inspecting missed objects, because low values produce many unstable masks.
5. Tune box_nms_thresh after the quality thresholds. A value of 0.3 can suppress nearby valid objects aggressively.

Also mask clouds and nodata, apply a consistent reflectance stretch to 8-bit RGB, and process large scenes as overlapping tiles. SAM2 is trained on natural RGB imagery and is not inherently multispectral or semantic: it will produce object masks, not Sentinel-2 land-cover classes. Validate parameter choices against labeled polygons using object recall/precision or IoU on representative tiles instead of selecting them visually from one scene.