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name: mobile-inference description: "Operate the documented MobileSAMv2 object-aware image inference route with explicit local weights, encoder selection, CUDA checks, and rendered output paths." metadata: disco-role: operating disable-model-invocation: true license: GPL 3.0

MobileSAMv2 object-aware inference

Use this route only for the separately documented MobileSAMv2 inference path: a local image directory is read, an object-aware detector supplies box prompts, and a prompt-guided SAM decoder renders masks. This is not the adapter-training CLI. Route training variants to training, and route image/dataset layout questions to data preparation.

Safe entry point

Run the bundled, preflight-only wrapper with explicit local paths:

python scripts/run_mobile_samv2.py \
  --ObjectAwareModel_path /abs/path/ObjectAwareModel.pt \
  --Prompt_guided_Mask_Decoder_path /abs/path/Prompt_guided_Mask_Decoder.pt \
  --encoder_path /abs/path/mobile_sam.pt \
  --encoder_type tiny_vit \
  --img_path /abs/path/images \
  --output_dir /abs/path/rendered \
  --preflight

--dry-run is an alias. Omitting both flags is also safe: this helper always performs preflight and never imports PyTorch, the detector, or MobileSAMv2 model code. It never downloads, creates output files, or performs inference. A passing result proves only the local path and static CLI contract; it is not a model or CUDA smoke test.

Operating sequence

1. Read the exact CLI. Select one of the three operational encoder mappings and supply every checkpoint path explicitly.
2. Run the wrapper and fix every reported path, extension, image, output, or encoder error before attempting any model import.
3. For a separately maintained real runner, use the repository-qualified package layout models.MobileSAMv2.mobilesamv2 and provide the detector dependency/import layout described in dependencies and weights. The optional prompt-guided detector module was not executed in verification.
4. On CUDA only, follow the source-equivalent workflow: convert OpenCV BGR images to RGB, detect boxes, transform boxes to the SAM input frame, decode in batches of 320, and render into the explicit output directory.
5. Keep input and output directories separate. Checkpoint files must be local, readable, and compatible with the selected builder; no implicit download is allowed.

Non-negotiable caveats

• CUDA is required for actual inference. The source initially places the model on CPU when CUDA is unavailable, but later creates the transformed box tensor with .cuda() unconditionally. CPU execution is therefore not supported; CPU parsing/import is diagnostic only.
• The original parser advertises six encoder strings, but the standalone source mapping actually contains only tiny_vit, sam_vit_h, and efficientvit_l2. The helper reports the other three as parser-accepted but operationally unsupported instead of allowing a later KeyError.
• The source parser's path defaults and create_model() disagree: the parsed paths are not consistently used, and some weights are hard-coded relative to the process directory. The wrapper intentionally removes that ambiguity by requiring explicit paths.
• Never let a missing detector input become source=None; the detector can choose a demo URL in that case. The wrapper accepts only local paths and has no network client.
• Do not copy the vendored Ultralytics or EfficientViT trees into this skill.

Verification boundary

The verified checks for this route are --help, deterministic parser behavior, local path/extension/weight/image/output validation, and no-network behavior. Full training, evaluation, detector/model imports, real checkpoints, rendered inference, notebook execution, and external downloads are not final checks and remain unrun because they have artifact, optional-dependency, CUDA, and output side effects. For shared failures, use the root troubleshooting guide.

See CLI reference, dependencies and weights, workflows, and troubleshooting.