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.