---

name: inference-and-postprocessing description: "Route BiRefNet image/video inference, mask saving, foreground refinement, and output validation." disable-model-invocation: true metadata: disco-role: operating license: MIT

inference-and-postprocessing

Use this sub-skill for BiRefNet inference tasks that need:

• a Hugging Face load path or a local .pth checkpoint
• image or directory-based mask generation
• foreground refinement or matting-style exports
• comparison panels and practical output validation
• notebook-style video inference guidance

Stay in scope

Use the bundled references and scripts when the task is about:

• loading BiRefNet weights for inference
• preparing Resize + ToTensor + ImageNet normalization
• running birefnet(input)[-1].sigmoid() in eval mode
• saving masks, foregrounds, or comparison images
• understanding native inference.py output layout

Route elsewhere

• Model/backbone internals, patch helpers, or export mechanics → ../model-architecture/SKILL.md
• Dataset layout or config defaults → ../configuration-and-data/SKILL.md
• Training, evaluation, metrics, or checkpoint selection → ../training-and-evaluation/SKILL.md

Runtime entry points

• references/inference-workflows.md
• references/video-workflows.md
• references/troubleshooting.md
• scripts/birefnet_image_infer.py
• scripts/birefnet_refine_smoke.py

Operating notes

1. Start with --help or --dry-run.
2. Pass --repo-root explicitly so imports stay anchored to the intended checkout.
3. Choose --model-source local for a checkpoint file or --model-source hf for hub weights.
4. Use --device auto, cpu, or cuda based on the available backend.
5. Keep --foreground-refine and --save-comparison enabled when you need matting-style outputs.
6. Local checkpoints should be cleaned with check_state_dict before loading; the helper does this for you.

Recommended operating flow

1. Confirm whether the task is single image, directory, video, or repo-style dataset inference.
2. Run scripts/birefnet_refine_smoke.py --device cpu if the user only needs postprocessing/backend sanity before loading model weights.
3. Run scripts/birefnet_image_infer.py --dry-run with the intended image directory, output directory, model source, device, and resolution before a real inference run.
4. For local checkpoints, route persistent key or tensor-size mismatches to ../model-architecture/SKILL.md.
5. For outputs that will be evaluated with BiRefNet metrics, preserve dataset/model directory names that ../training-and-evaluation/SKILL.md expects.

Done criteria

• The model source and required assets are explicit: Hugging Face repo/cache or local .pth checkpoint.
• The device, resolution, and CPU/CUDA/autocast limits are stated.
• The expected mask, foreground, and comparison output directories are known.
• Optional foreground refinement and video processing are separated from the core mask inference step.

Output conventions

• Masks go to masks/
• Foreground/matting exports go to foregrounds/
• Comparison panels go to comparisons/
• Relative folder structure under --input is preserved