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
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).

Splits

A single train split is provided; users should split as needed (recommended: stratify by scene_family / n_cable).

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.