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name: automatic-mask-generation description: "Generate masks for all objects in images or folders with Segment Anything's SamAutomaticMaskGenerator and the bundled AMG CLI. Use for batch automatic masks, PNG/CSV outputs, COCO RLE JSON, threshold tuning, crop settings, and memory-aware AMG runs." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Automatic Mask Generation

Use this sub-skill when the user wants SAM to segment all visible objects in an image or folder without point or box prompts. It covers SamAutomaticMaskGenerator, the bundled scripts/amg_cli.py, binary mask PNG folders, metadata.csv, COCO RLE JSON, threshold tuning, crop expansion, batching, and memory tradeoffs.

Do not use this sub-skill for prompted point/box/mask refinement; route those requests to ../prompted-segmentation/. Do not use it for ONNX export, browser inference, or the web demo; route those requests to ../onnx-and-browser/.

Quick Start

python sub-skills/automatic-mask-generation/scripts/amg_cli.py \
  --checkpoint sam_vit_b_01ec64.pth \
  --model-type vit_b \
  --input images/ \
  --output masks/ \
  --device cpu

For COCO-style RLE JSON instead of per-mask PNG folders:

python sub-skills/automatic-mask-generation/scripts/amg_cli.py \
  --checkpoint sam_vit_h_4b8939.pth \
  --model-type vit_h \
  --input image.jpg \
  --output masks-rle/ \
  --convert-to-rle \
  --device cuda

Routing

• Use references/api-reference.md for direct Python use of SamAutomaticMaskGenerator and returned annotation records.
• Use references/cli-reference.md for exact bundled CLI flags, output layout, and optional dependency checks.
• Use references/workflows.md for folder runs, COCO RLE conversion, threshold tuning, and avoiding GPU out-of-memory failures.
• Use references/troubleshooting.md for missing cv2, missing pycocotools, checkpoint/model mismatch, CPU fallback, unreadable images, empty output, and memory blowups.

Key Defaults

• Registry keys are default, vit_h, vit_l, and vit_b; default is equivalent to the ViT-H builder.
• SamAutomaticMaskGenerator(model) defaults to points_per_side=32, points_per_batch=64, pred_iou_thresh=0.88, stability_score_thresh=0.95, crop_n_layers=0, min_mask_region_area=0, and output_mode="binary_mask".
• output_mode="coco_rle" requires pycocotools; min_mask_region_area > 0 requires OpenCV.
• Large images, high points_per_side, large points_per_batch, crop layers, and binary mask output all increase memory use.