RampNet Stage 1 Crop Model

The Stage 1 crop model from RampNet: A Two-Stage Pipeline for Bootstrapping Curb Ramp Detection in Streetscape Images from Open Government Metadata (O'Meara et al., ICCV'25 CV4A11y workshop, arXiv:2508.09415).

This is not the curb ramp detector — that is projectsidewalk/rampnet-model. This is the model that makes the training data for it: given a government-published curb ramp GPS coordinate and the street-view panorama nearest it, it predicts where in that panorama the ramp actually appears. Every one of the 849,895 keypoint labels in rampnet-dataset was placed by this model.

Stage 1 cannot be reproduced without it. stage_one/dataset_generation/inference_isolator.py loads the round-2 checkpoint by a hardcoded relative path; the government inventories and street data in the training repo are inert without it.

The two rounds

Training is two-stage, and both checkpoints are published because round 1 is the initialisation for round 2 — without it, the second round cannot be reproduced either.

round	file	trained on	role
1	`round1_ps_best_model.pth`	Project Sidewalk crops	pre-training
2	`round2_ps_and_manual_best_model.pth`	+ manually labeled crops (`rampnet-crop-model-dataset`)	the one Stage 1 loads

Provenance

Field	Value
Training code	https://github.com/ProjectSidewalk/RampNet @ `cd70f05`
Round 1 sha256	`00dba3948298a313435b7c1955a2d4fccde43bc98c199e384ef197bf8b8cff49`
Round 2 sha256	`3fc00ad6b9ac2768787b0262588b9bfa71ddd01d9f51109974e6ae377b9b520a`
Exported	2026-08-04 by `scripts/export_crop_model.py`

These are the paper-era checkpoints, recovered from cluster storage — the artifacts that produced the published dataset, not a retrain.

A note recorded because it is easy to get wrong when reproducing the pipeline: in the original run, round 1's best_model.pth was copied into the round-2 directory renamed to ps_model.pth. Those two files are byte-identical (verified by sha256), so round1_ps_best_model.pth here serves both purposes.

Architecture

A timm convnextv2_base.fcmae_ft_in22k_in1k_384 backbone with a small conv + bilinear-upsample head producing a single-channel keypoint heatmap — the same KeypointModel class as the Stage 2 detector, differing only in heatmap_size.

	crop model (this)	Stage 2 detector
input	1024 x 352	2048 x 4096
heatmap	256 x 88	512 x 1024

Usage

Each round ships in two formats, same weights:

file	use it for
`*.safetensors`	prefer this. Loading it cannot execute code
`*.pth`	the original `torch.save` artifact, kept because its sha256 above is what ties this to the paper's run — and it is what `inference_isolator.py` loads unmodified

The .pth files are pickle archives, so torch.load on them is only as safe as your trust in the source; that is exactly why the safetensors copies exist. They were produced by scripts/export_crop_model.py, which compares every tensor after the round trip and refuses to write on any mismatch.

Preferred load:

from safetensors.torch import load_file
from rampnet.model import KeypointModel, CROP_HEATMAP_SIZE

model = KeypointModel(heatmap_size=CROP_HEATMAP_SIZE)      # (256, 88)
model.load_state_dict(load_file("round2_ps_and_manual_best_model.safetensors"))
model.eval()

To reproduce Stage 1 unmodified, put the .pth where inference_isolator.py expects it:

hf download projectsidewalk/rampnet-crop-model round2_ps_and_manual_best_model.pth --local-dir .
mv round2_ps_and_manual_best_model.pth \
   RampNet/stage_one/crop_model/ps_and_manual_model/best_model.pth

Limitations

The round-1 training set is not reproducible. stage_one/crop_model/ps_model/data/download_data.py reads live from Project Sidewalk servers with no snapshot pinning, and those databases keep growing, so re-running it builds a different crop set than the paper's.
Trained on Project Sidewalk cities and used on NYC / Portland / Bend panoramas; see the contamination registry in docs/data_provenance.md before evaluating any RampNet-derived model in those cities.

Citation

@inproceedings{omeara2025rampnet,
  author    = {John S. O'Meara and Jared Hwang and Zeyu Wang and Michael Saugstad and Jon E. Froehlich},
  title     = {{RampNet: A Two-Stage Pipeline for Bootstrapping Curb Ramp Detection in Streetscape Images from Open Government Metadata}},
  booktitle = {{ICCV'25 Workshop on Vision Foundation Models and Generative AI for Accessibility: Challenges and Opportunities (ICCV 2025 Workshop)}},
  year      = {2025},
  doi       = {https://doi.org/10.48550/arXiv.2508.09415},
}