---
license: cdla-permissive-2.0
task_categories:
  - object-detection
  - image-segmentation
tags:
  - civil-infrastructure
  - inspection
  - crack-detection
  - aerial
pretty_name: "Cracks in the Foundation"
size_categories:
  - 100K<n<1M
dataset_info:
  splits:
    - name: train_full
      num_examples: 9096
    - name: val_full
      num_examples: 1942
    - name: test_full
      num_examples: 1858
    - name: train_tiled
      num_examples: 105139
    - name: val_tiled
      num_examples: 22368
    - name: test_tiled
      num_examples: 21135
configs:
  - config_name: default
    data_files:
      - split: train_full
        path: data/train_full-*
      - split: val_full
        path: data/val_full-*
      - split: test_full
        path: data/test_full-*
      - split: train_tiled
        path: data/train_tiled-*
      - split: val_tiled
        path: data/val_tiled-*
      - split: test_tiled
        path: data/test_tiled-*
  - config_name: train_full
    data_files:
      - split: train
        path: data/train_full-*
  - config_name: val_full
    data_files:
      - split: train
        path: data/val_full-*
  - config_name: test_full
    data_files:
      - split: train
        path: data/test_full-*
  - config_name: train_tiled
    data_files:
      - split: train
        path: data/train_tiled-*
  - config_name: val_tiled
    data_files:
      - split: train
        path: data/val_tiled-*
  - config_name: test_tiled
    data_files:
      - split: train
        path: data/test_tiled-*
---

# Cracks in the Foundation

A civil-infrastructure visual inspection dataset for instance segmentation with 6 defect/condition categories:
**Algae** · **Crack** · **Net-Crack** · **Crack with Precipitation** · **Rust** · **Spalling**

Each sample is either a full-resolution inspection image or a 1024×1024 tile derived from one.
Tiled samples carry extra fields (`tile_row`, `tile_col`, `file_name_original`, …) that are `None` for full-resolution samples.

---

## 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 |
|---|---|
| `train_full` | full-resolution training images |
| `val_full` | full-resolution validation images |
| `test_full` | full-resolution test images |
| `train_tiled` | 1024×1024 tiles, training |
| `val_tiled` | 1024×1024 tiles, validation |
| `test_tiled` | 1024×1024 tiles, test |

---

## Load

```python
from datasets import load_dataset

# Load all six splits at once (single DatasetDict):
all_splits = load_dataset("ibm-research/cif-dataset")

# Selective: download only the tiled training shard.
# Each split is also exposed as its own config — naming a config
# downloads only its parquet files.
ds = load_dataset("ibm-research/cif-dataset", "train_tiled", split="train")
```

---

## Schema

Every sample has the same fields regardless of split:

```python
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:

```python
is_tile = sample["tile_row"] is not None
```

---

## Visualize

```bash
pip install datasets fiftyone
```

```python
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("ibm-research/cif-dataset", split="test_full")

tmp = Path(tempfile.mkdtemp())
fo_ds = fo.Dataset("cif_test_full", 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()
```

Opens the FiftyOne app at `http://localhost:5151` with bounding boxes and segmentation overlays.

---

## Acknowledgment

We would like to sincerely thank Finn Bormlund and Svend Gjerding (Sund & Baelt), Jens Häggström (Trafikverket), Raphael von Thiessen (Innovation-Sandbox for AI, Office for Economy, Kanton Zürich), and the Dübendorf Air Base for granting us the opportunity to collect, analyze, and disseminate the images and defect data included in this publication.

---

## Citation

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