nicolaaaaa/cif_dataset_toy

Cracks in the Foundation — Toy

A compact, reproducible sample of the Cracks in the Foundation dataset for quick experimentation and review. It covers the same 6 defect/condition categories as the full dataset:

Algae · Crack · Crack (net-crack) · Crack with precipitation · Rust · Spalling

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Splits

Each split is its own parquet shard and its own dataset config. load_dataset(repo) returns all six in a DatasetDict. Naming a config — load_dataset(repo, "train_tiled", split="train") — is a true selective download, fetching only that shard.

Split	Contents	Size
train_full	full-resolution training images	100
val_full	full-resolution validation images	100
test_full	full-resolution test images	100
train_tiled	1024×1024 tiles derived from training images	100
val_tiled	1024×1024 tiles derived from validation images	100
test_tiled	1024×1024 tiles derived from test images	100

Full-resolution and tiled images are kept in separate splits so you can download only what you need.

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Sampling

Each split was sampled from the corresponding split of the full dataset using:

images_sorted = sorted(source_images, key=lambda x: x["id"])
sample = random.Random(42).sample(images_sorted, 100)

• Method: uniform random without replacement
• Seed: 42 (fixed)
• Input order: source images sorted ascending by COCO image_id before sampling, so the draw is stable regardless of JSON ordering

Rebuilding from the same source data and seed always yields the same 100 images per split.

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Load

from datasets import load_dataset

# Load all six splits at once (single DatasetDict):
all_splits = load_dataset("nicolaaaaa/cif_dataset_toy")

# Selective: download only the tiled test shard.
# Each split is also exposed as its own config — naming a config
# downloads only its parquet files.
ds = load_dataset("nicolaaaaa/cif_dataset_toy", "test_tiled", split="train")

# Selective: download only the full-resolution training shard.
ds = load_dataset("nicolaaaaa/cif_dataset_toy", "train_full", split="train")

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Schema

Every sample has the same fields regardless of split:

sample = ds[0]

sample["image_id"]           # int   — unique image identifier
sample["image"]              # PIL.Image
sample["file_name"]          # str   — original filename
sample["width"]              # int   — image width in pixels
sample["height"]             # int   — image height in pixels

# Tiled-only fields (None for full-resolution samples):
sample["tile_row"]           # int | None  — top-left row of the tile in the original image
sample["tile_col"]           # int | None  — top-left column
sample["file_name_original"] # str | None  — filename of the parent image
sample["width_original"]     # int | None  — parent image width
sample["height_original"]    # int | None  — parent image height

# Annotations (COCO convention):
obj = sample["objects"]
obj["id"]            # List[int]
obj["category_id"]   # List[int]   — 1=Algae 2=Crack 3=Crack(net) 4=Crack+precip 5=Rust 6=Spalling
obj["bbox"]          # List[[x, y, w, h]]   — pixels, COCO origin (top-left)
obj["area"]          # List[float]
obj["iscrowd"]       # List[int]
obj["segmentation"]  # List[List[List[float]]]  — polygons as flat [x1,y1,x2,y2,...] lists

Distinguish sample type at runtime:

is_tile = sample["tile_row"] is not None

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Visualize

import tempfile
from pathlib import Path

import fiftyone as fo
from datasets import load_dataset

CATS = {1: "Algae", 2: "Crack", 3: "Crack (net-crack)",
        4: "Crack with precipitation", 5: "Rust", 6: "Spalling"}

ds = load_dataset("nicolaaaaa/cif_dataset_toy", split="test_tiled")

tmp = Path(tempfile.mkdtemp())
fo_ds = fo.Dataset("cif_toy_test_tiled", overwrite=True)

for s in ds:
    img_path = tmp / Path(s["file_name"]).name
    s["image"].save(img_path)
    W, H = s["width"], s["height"]
    dets, polys = [], []
    obj = s["objects"]
    for i, cid in enumerate(obj["category_id"]):
        label = CATS.get(cid, str(cid))
        x, y, w, h = obj["bbox"][i]
        dets.append(fo.Detection(label=label, bounding_box=[x/W, y/H, w/W, h/H]))
        for poly in obj["segmentation"][i]:
            if len(poly) < 6:
                continue
            pts = [[poly[j]/W, poly[j+1]/H] for j in range(0, len(poly), 2)]
            polys.append(fo.Polyline(label=label, points=[pts], filled=True, closed=True))
    fo_ds.add_sample(fo.Sample(
        filepath=str(img_path),
        detections=fo.Detections(detections=dets),
        segmentations=fo.Polylines(polylines=polys),
    ))

session = fo.launch_app(fo_ds)
session.wait()

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Full dataset

The full dataset (all images, all splits) is available at ibm-research/cif-dataset. It has the identical schema and split naming convention.

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Citation

@dataset{cracks_in_the_foundation,
  author    = {},
  title     = {Cracks in the Foundation},
  year      = {2025},
  publisher = {HuggingFace},
  url       = {https://huggingface.co/datasets/ibm-research/cif-dataset},
}