---
pipeline_tag: image-segmentation
tags:
  - onnx
  - roomorph
  - semantic-segmentation
  - browser
---

# RooMorph room surface segmentation (ONNX)

Browser model used by RooMorph to classify room pixels as other, wall, floor, or ceiling. This repository hosts the existing exported weights; it is not a new training run.

## Files and identity

- `model.onnx`: 23,288,287 bytes.
- SHA-256: `72d176cbe3e111551a04b21929a4c3bc564c24ed57c45f1529519323f6571ac7`.
- `metadata.json`: input/output contract, preprocessing, class labels, and model identity. Use this file as the implementation reference.
- `provenance.json`: retained source, training-data provenance declarations, and limitations.
- `MODEL_IMPLEMENTATION_LICENSE.txt`: retained implementation license notice.

## Inference

The model is a custom trained DeepLabV3+ MobileNet export. The float32 RGB input is NCHW `[1, 3, 513, 513]`, letterboxed as specified in metadata, scaled by 1/255 and normalized with ImageNet mean and standard deviation. RooMorph performs inference in the user's browser. This repository provides download hosting, not an inference API.

## Provenance and limitations

The existing provenance identifies Qualcomm / AIMET model zoo pretrained weights and a Roboflow room segmentation dataset. Its declared licenses and the original implementation notice are retained without assigning a new license to the weights. The dataset provenance records that source-image rights and commercial rights were not independently verified. Consult `provenance.json` and the original sources for details.

No new accuracy, benchmark, robustness, or commercial-rights claims are made by this hosting migration. Outputs can require correction and should be interpreted within RooMorph's visualization workflow.

Production consumers should pin downloads to a full commit revision and verify the SHA-256 above.