---

name: u-2-net description: "Route U-2-Net salient object detection, human segmentation, portrait generation, architecture, and training-preparation workflows." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

U-2-Net Repo Skill

Use this skill when the task is about U-2-Net/U2NET PyTorch workflows: salient object detection masks, human/person segmentation, portrait drawing, portrait compositing, model architecture/checkpoints, dependency checks, or DUTS-style training preparation.

First checks

• This generated skill bundles distilled U-2-Net model code and helper scripts. It does not bundle pretrained .pth weights.

• Core workflows need Python with torch, torchvision, numpy, Pillow, scikit-image, and usually cv2 for portrait own-image mode.

• A typical CPU-oriented install is:

  python -m pip install torch torchvision numpy pillow scikit-image opencv-python matplotlib
  

  Choose a CUDA-enabled PyTorch install only when the user explicitly needs GPU acceleration and the host supports it.

• Run scripts/check_environment.py when debugging imports or optional CUDA.

• Read model weights before choosing a checkpoint.

• Read repo provenance when checking staleness against a new checkout.

Route map

User task	Read
Choose between U2NET, U2NETP, side outputs, checkpoint compatibility, or run a tiny architecture smoke test.	sub-skills/model-architecture/SKILL.md
Run generic saliency masks with u2net/u2netp, human/person segmentation, or troubleshoot mask outputs.	sub-skills/salient-object-inference/SKILL.md
Run portrait drawing, own-photo face-crop portrait inference, Haar cascade handling, or sigma/alpha composites.	sub-skills/portrait-workflows/SKILL.md
Validate DUTS-style data layout, inspect preprocessing, or plan bounded retraining.	sub-skills/data-and-training/SKILL.md
Optional PaddleHub/Gradio web demo.	references/optional-demos.md

Minimal dependency check

python scripts/check_environment.py --check-cuda

For architecture-only smoke testing from this skill directory:

python sub-skills/model-architecture/scripts/smoke_architecture.py --model u2netp --height 64 --width 64 --device cpu

For pretrained saliency, supply explicit weights:

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

Cross-cutting references

• Troubleshooting: install/import, missing weights, optional CUDA/PaddleHub, script-style repo, and training safety.
• Model weights: checkpoint-to-workflow matrix and no-download policy.
• Optional demos: PaddleHub/Gradio route and why it is not part of the minimum workflow.
• Routing metadata: structured scenario placement for managed repo-skill import.

Safety and scope

Do not start large downloads, full training, web demos, or checkpoint overwrites without explicit user approval. Use random-weight smoke flags only for dependency and plumbing checks; never present random outputs as U-2-Net predictions.