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name: salient-object-inference description: "Run U-2-Net salient object and human segmentation inference from image folders to PNG masks with explicit weights and safe diagnostics." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Salient Object Inference

Use this sub-skill when the user needs U-2-Net image-folder inference that writes PNG masks: generic salient object detection with u2net or u2netp, or human/person segmentation with a u2net_human_seg.pth checkpoint.

Start here

1. Choose the task and matching checkpoint from model weights.
2. Run the bundled helper scripts/u2net_infer.py instead of original demo scripts.
3. Use workflows for command recipes, preprocessing/output behavior, and validation checks.
4. Use troubleshooting for missing weights, empty input folders, checkpoint mismatches, blank masks, NaNs, CUDA/device issues, and output naming surprises.

Common routes

• Fast CPU saliency on a custom image folder: use --task saliency --model u2netp --device cpu --weights ... --input-dir ... --output-dir ....
• Full saliency model: use --task saliency --model u2net and a u2net.pth checkpoint.
• Human segmentation: use --task human with u2net_human_seg.pth; human mode uses full U2NET(3, 1), not U2NETP.
• Plumbing-only smoke checks: add --allow-random-weights-for-smoke --max-images 1 only to verify imports, preprocessing, model forward, and mask writing. Do not present random-weight outputs as meaningful predictions.

Boundaries

• Portrait generation, face cropping, drawing-style portrait prediction, and compositing belong to portrait-workflows.
• Training data layout, transforms for training, and retraining plans belong to data-and-training.
• Architecture internals, refactored builders, side-output semantics, and checkpoint-shape debugging belong to model-architecture.

Minimal command shape

python scripts/u2net_infer.py \
  --task saliency \
  --model u2netp \
  --weights PATH_TO_WEIGHTS/u2netp.pth \
  --input-dir INPUT_IMAGES \
  --output-dir OUTPUT_MASKS \
  --device cpu

The helper writes one RGB PNG mask per input image using the input filename stem, resized back to the original image dimensions.