Building Inspection Toolkit

Building Inspection Toolkit helps you with machine learning projects in the field of damage recognition on built structures, currenlty with a focus on bridges made of reinforced concrete.

• DataHub: It contains curated open-source datasets, with fix train/val/test splits (as this is often missing) and cleaned annotations. It is build for PyTorch.
• Metrics: We define useful metrics you can use and report to make comparability easier.
• Pre-trained Models: We provide strong baseline models for different datasets. See bikit-models for more details.

The Datasets

Open-source data

Name | Type | Unique images | train/val/test split ----------|-------------|---------------|------------- CDS [Web] | 2-class single-target Clf | 1,028 | bikit-version BCD [Paper] [Data] | 2-class single-target Clf | 6069 | modified-version SDNET [Web] | 2-class single-target Clf | 56,092 | bikit-version MCDS [Paper] [Data] | 8-class multi-target Clf | 2,612 | bikit-version CODEBRIM [Paper] [Data] | 6-class multi-target Clf | 7,730 | original-version

Bikit datasets

Different versions

For some datasets different versions exists. This may be due to the fact that the authors already provide different version (e.g. CODEBRIM) or other authors update datasets (e.g. Bukhsh for MCDS).

Splits

We provide carefully selected train/val/test splits. We introduce splits, when they are not available or update splits where we think this is useful.

Overview

| name | Note | | ----------------------------|-------------------------------------------------------------------------------------------------------| | dacl1k | 6-class multi-target dataset. | | cds | Original dataset with bikit's train/val/test splits
| bcd | Original dataset with modified train/val/test splits (original train was splitted into train/val)
| sdnet | Original dataset with bikit's train/val/test splits; Many wrong labels
| sdnet_binary | Many wrong labels; Binarized version of sdnet: crack, no crack | sdnet_bikit | Cleaned wrong labels
| sdnet_bikit_binary | Cleaned wrong labels; Binarized version of sdnet: crack, no crack
| mcds_Bukhsh | Bukhsh et al. create a 10-class dataset out of the 3-step dataset from Hüthwohl et al. (with same wrong labels); With bikit's train/val/test splits | | mcds_bikit | We create a 8-class dataset from Hüthwohl et al. which prevent wrong labels with bikit's train/val/test splits. | codebrim-classif-balanced | Original dataset with original train/val/test splits: Underrepresented classes are oversampled. | | codebrim-classif | Original dataset with original train/val/test splits. | | meta2 | 6-class multi-target dataset based on codebrim-classif, and mcdsbikit. | | meta3 | 6-class multi-target dataset based on bcd, codebrim-classif, and mcdsbikit. | | meta4 | 6-class multi-target dataset based on bcd, codebrim-classif, mcdsbikit, and sdnetbikitbinary. | | meta2+dacl1k | 6-class multi-target dataset based on dacl1k, codebrim-classif, and mcdsbikit. | | meta3+dacl1k | 6-class multi-target dataset based on dacl1k, bcd, codebrim-classif, and mcdsbikit. | | meta4+dacl1k | 6-class multi-target dataset based on dacl1k, bcd, codebrim-classif, mcdsbikit, and sdnetbikitbinary. |

Usage

Data

List and download data

```python from bikit.utils import listdatasets, downloaddataset

List all datasets

alldataset = listdatasets(verbose=0) all_dataset.keys()

Download data

DATASETNAME = "mcdsbikit" downloaddataset(DATASETNAME) ```

Use BikitDataset

```python from bikit.utils import download_dataset from bikit.datasets import BikitDataset from torch.utils.data import DataLoader from torchvision import transforms

Select a dataset:

name = "mcds_bikit"

downloaddataset(name) # equals to `downloaddataset("mcdsBukhsh")` mytransform = transforms.Compose([transforms.Resize((256,256)), transforms.ToTensor()])

Use return_type 'pt' (default) or 'np'

traindataset = BikitDataset(name, split="train", transform=mytransform, returntype="pt") trainloader = DataLoader(dataset=traindataset, batchsize=64, shuffle=False, num_workers=0)

Use it in your training loop

for i, (imgs, labels) in enumerate(train_loader): print(i, imgs.shape, labels.shape) break ```

Metrics

• For single-target problems (like cds, bcd, sdnet_bikit_binary) metrics will follow (#TODO).
• For multi-target problems (like sdnet_bikit, mcds_bikit or meta3) we use Exact Match Ratio (EMR_mt) and classwise Recall (Recalls_mt).

```python

!pip install torchmetrics

from bikit.metrics import EMRmt, Recallsmt import torch

myemr = EMRmt(uselogits=False) myrecalls = Recalls_mt()

fake data

preds0 = torch.tensor([[.9, 0.1, 0.9, 0.1, 0.9, 0.1], [.8, 0.2, 0.9, 0.2, 0.9, 0.2], [.7, 0.9, 0.2 , 0.2, 0.2 , 0.2]]) preds1 = torch.tensor([[.0, 0.1, 0.9, 0.1, 0.9, 0.1], [.8, 0.2, 0.9, 0.2, 0.9, 0.2], [.7, 0.9, 0.2 , 0.9, 0.2 , 0.9]]) target = torch.tensor([[1, 0, 1, 0, 0, 1], [1, 1, 0, 0, 1, 0], [1, 1, 0, 1, 0, 1]])

batch 0

myemr(preds0, target), myrecalls(preds0, target) print(myemr.compute(), myrecalls.compute())

batch 1

myemr(preds1, target), myrecalls(preds1, target)
print(myemr.compute(), myrecalls.compute())

Reset at end of epoch

myemr.reset(), myrecalls.reset() print(myemr, myrecalls) ```

Models

List models

```python from bikit.utils import list_models

List all models

List all models

allmodels = listmodels(verbose=0) all_models.keys() ```

Model Inference

```python from bikit.utils import loadmodel, getmetadata, loadimgfromurl from bikit.models import makeprediction from matplotlib import pyplot as plt

Download and load model

model, metadata = loadmodel("MCDSbikitResNet50dhb", addmetadata=True)

img = loadimgfromurl("https://github.com/phiyodr/building-inspection-toolkit/raw/master/bikit/data/11001990.jpg") plt.imshow(np.asarray(img)) prob, pred = makeprediction(model, img, metadata, printpredictions=True, preprocess_image=True)

> Crack [██████████████████████████████████████ ] 95.79%

> Efflorescence [ ] 0.56%

> ExposedReinforcement [ ] 0.18%

> General [ ] 0.61%

> NoDefect [ ] 1.31%

> RustStaining [ ] 0.44%

> Scaling [ ] 0.05%

> Spalling [ ] 0.86%

> Inference time (CPU): 207.96 ms

```

Installation

bash !pip install git+https://github.com/phiyodr/building-inspection-toolkit !pip install patool !pip install efficientnet-pytorch !pip install torchmetrics

Misc

PyTest

Install dependencies first

bash pip3 install -U -r requirements.txt -r test_requirements.txt

Run PyTest

```bash

cd bridge-inspection-toolkit/

pytest ```

Citation

Use the "Cite this repository" tool in the About section of this repo to cite us :)