README.md

metadata

license: cc-by-4.0
pretty_name: WireSeg-36K
task_categories:
  - image-segmentation
  - depth-estimation
tags:
  - DLO
  - segmentation
  - cables
  - wire
  - sam
  - real image
  - synthetic image
size_categories:
  - 10K<n<100K
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/*.parquet
  - config_name: real
    data_files:
      - split: test
        path: real/*.parquet

WireSeg-36K

A 36,000-image dataset for cable / wire instance segmentation: physically simulated deformable linear objects (DLOs) rendered over diverse backgrounds, with exact per-instance masks and per-image metric depth. Single category: cable. Generated with DeformX.

• Images: 36,000 (1024x1024 RGB)
• Instances: 176,387 cable masks (COCO RLE)
• Categories: 1 (cable)
• Modalities: RGB, instance masks, per-image metric depth

Scenes

scene_family	cable counts	images	description
data_center	4 / 8 / 16	6,000	wires routed on server racks
flying_wire	2 / 4 / 8	15,000	suspended / catenary wires
wire_on_plane	2 / 4 / 8	15,000	wires draped on a flat surface

Files & layout

path	what
data/*.parquet (config default)	RGB image + per-instance COCO-RLE masks + metadata, one row per image
real/*.parquet (config real)	real-image eval subset (300 imgs) — RGB + COCO-RLE masks, one row per image
annotations.json	full COCO ground truth (36,000 images / 176,387 annotations, single category cable)
depth/*.tar.gz	per-image metric depth, float32 [1024,1024] .npy, packed in 100 gzipped tar shards

The image-only branch holds the RGB+masks release without depth.

Usage

from datasets import load_dataset
ds = load_dataset("DeformX/WireSeg-36K", split="train")   # rgb + masks + metadata
ex = ds[0]
ex["image"]                              # PIL.Image (RGB)

import json
from pycocotools import mask as M
masks = json.loads(ex["masks_rle"])      # list of {"size":[h,w], "counts": <RLE str>}
m0 = M.decode(masks[0])                   # HxW uint8 instance mask

Depth. Download and extract the depth/*.tar.gz shards; each member is <scene_family>/<n>_cable/<id>.npy. The depth for an image is the .npy at the same relative path as its file_name (swap .png -> .npy):

import numpy as np
d = np.load(f"depth/{ex['file_name'][:-4]}.npy")   # float32 [1024,1024], metric

Data fields (Parquet default)

image (RGB), file_name, scene_family, n_cable, masks_rle, bboxes, areas, num_instances, wire_area_frac, det_f1_iou_50, det_f1_iou_75, det_map_50_95, jaccard_j, category. Masks / bboxes / areas are JSON-encoded per-instance lists (RLE = COCO compressed).

Real eval subset (config real)

A companion set of 300 real cable images with hand-verified ground truth, for evaluating sim-to-real transfer of the synthetic default set. These were cherry-picked as the disagreement slice between base SAM 3 (prompt "cable") and a cable-tuned LoRA — i.e. the images the two models most disagreed on, so they concentrate the hard/informative cases. Masks are COCO-RLE, single category cable.

• Images: 300 real (variable resolution) — scene_family = real_easy (224) / real_hard (76)
• Instances: 2,119 cable masks (COCO RLE)

from datasets import load_dataset
real = load_dataset("DeformX/WireSeg-36K", "real", split="test")
ex = real[0]                                  # ex["image"] -> PIL.Image

Fields: image, file_name, scene_family, masks_rle, bboxes, areas, num_instances, category, width, height — the mask/bbox/area columns match the default config (same JSON-encoded per-instance COCO-RLE), so decoding code is shared. The synthetic-only QA columns (n_cable, wire_area_frac, det_*, jaccard_j) are omitted; width/height are added because real images are not a fixed size.

Splits

The default config provides a single train split; users should split as needed (recommended: stratify by scene_family / n_cable). The real config is a held-out test set.

Generation

Wires are simulated and rendered with DeformX and composited over background imagery; ground-truth instance masks and metric depth come directly from the renderer (no human annotation), exported to COCO RLE.

Limitations & responsible use

• Synthetic foreground; expect a domain gap to real imagery.
• Backgrounds are real photographs composited in; they may contain incidental scene content. Redistribution is under the dataset license below.

Citation

@inproceedings{yang2026deformx,
  title     = {DeformX: A Versatile Co-Simulation Framework for
               Deformable Linear Objects},
  author    = {Yang, Yi and Fei, Xiang and Wang, Lehong and Li, Chenhao
               and Dai, Zilin and Kou, Henry and Li, Lu and Choset, Howie},
  booktitle = {2026 IEEE/RSJ International Conference on Intelligent
               Robots and Systems (IROS)},
  year      = {2026},
  organization = {IEEE}
}

License

cc-by-4.0.