Datasets:

aviadcohz
/

RWTD

license: cc-by-4.0
task_categories:
  - image-segmentation
tags:
  - texture
  - boundary-detection
  - segmentation
  - materials
size_categories:
  - n<1K

RWTD - Real World Texture Boundary Dataset

A dataset of 253 image crops (256x256), each containing exactly two adjacent textures separated by a boundary. Every sample includes per-texture binary segmentation masks and natural-language texture descriptions.

Dataset Description

Each sample contains:

Field	Type	Description
`image`	Image	256x256 RGB crop
`boundary_mask`	Image	Binary boundary between the two textures
`texture_a_mask`	Image	Binary mask for texture A
`texture_b_mask`	Image	Binary mask for texture B
`texture_a`	string	Natural-language description of texture A
`texture_b`	string	Natural-language description of texture B
`original_texture_a`	string	Short original label for texture A
`original_texture_b`	string	Short original label for texture B
`crop_name`	string	Sample identifier
`oracle_points_a`	string	JSON-encoded list of [x,y] point prompts for texture A
`oracle_points_b`	string	JSON-encoded list of [x,y] point prompts for texture B

Texture Descriptions

Each texture region is annotated with a rich 5-10 word description emphasizing discriminative visual features (material, pattern, structure). Examples:

"smooth curved seashell with radiating ridged lines"
"fine sandy substrate with granular particles"
"rough wood grain with prominent raised texture"
"quilted padded fabric with raised diamond stitching"

Usage

from datasets import load_dataset

ds = load_dataset("aviadcohz/RWTD")

sample = ds["train"][0]
print(sample["texture_a"], "—", sample["texture_b"])
sample["image"].show()

Statistics

253 samples
256x256 pixel crops
2 textures per image with complementary binary masks
Rich texture descriptions (5-10 words each)
Oracle point prompts for each texture region