GT-CrackSeg

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<div align="center">
  <h1>GT-CrackSeg</h1>
  <p>A Modular PyTorch Framework for Crack Segmentation Research</p>
</div>

## News
- [2025-12-30] Check out our comprehensive review paper [Deep Learning for Crack Detection: A Review of Learning Paradigms, Generalizability, and Datasets](https://arxiv.org/abs/2508.10256)
- [2025-12-30] Our curated cracking paper list [Awesome-Crack-Detection](https://github.com/nantonzhang/Awesome-Crack-Detection/tree/master)
- [2025-12-30] 📢 We released the first version of GT-CrackSeg!

## Features

- Predefined models, losses, and training settings ready to run  
- Supports popular architectures such as ResUNet, DeepLabV3, and SegFormer
- Multi-GPU training support with PyTorch DistributedDataParallel  
- Built-in training with validation, checkpointing, and performance metrics

## Installation
```sh
# Create the 'crackseg' environment
conda env create -f environment.yml

# Activate the environment
conda activate crackseg
```

## Usage

```sh
# All arguments are optional
# The default model is DeepLabV3, the DeepCrack dataset, and the Dice loss function

python -m main [-c MODEL_CONFIG] [--dataset DATASET] [--loss LOSS]
```

**Examples**
```sh
# Run with the default arguments
python -m main

# Train ResUNet on DeepCrack
python -m main -c resunet.yml --dataset deepcrack

# Train DeepLabV3 on CrackTree with Dice loss
python -m main -c deeplabv3.yml --dataset cracktree --loss dice
```
![](https://raw.githubusercontent.com/GT-SCI/GT-CrackSeg/main/assets/example_usage.gif)

## Multi-GPU training

We support multi-GPU training via `torchrun` and PyTorch DistributedDataParallel

```sh
# 4 GPUs on a single node
torchrun --nproc_per_node=4 main.py -c cracksam.yml --dataset khanhha
```

## Supported Models

Each model includes a standalone configuration file defining its architecture and training settings

| Model         | Config                                         |
|---------------|------------------------------------------------|
| ResUNet       | [resunet.yml](configs/resunet.yml)             |
| DeepLabV3     | [deeplabv3.yml](configs/deeplabv3.yml)         |
| SegFormer     | [segformer.yml](configs/segformer.yml)         |
| DPT           | [dpt.yml](configs/dpt.yml)                     |
| CrackFormerII | [crackformerii.yml](configs/crackformerii.yml) |
| SCSegamba     | [scsegamba.yml](configs/scsegamba.yml)         |
| CrackSAM      | [cracksam.yml](configs/cracksam.yml)           |

## Datasets

### Supported Datasets
This framework is compatible with the following crack segmentation benchmark datasets.

**Download:** [Google Drive (all datasets)](https://drive.google.com/drive/folders/113yIk8Yy0LluPJ9a79Rj7RVMxwk3EZwP?usp=sharing)
- Please follow each dataset's original license/terms.
- After downloading, unzip into `data/` so paths match the structure below.

| Dataset   | Example Usage         |
|-----------|-----------------------|
| DeepCrack | `--dataset deepcrack` |
| TUT       | `--dataset tut`       |
| Khanhha   | `--dataset khanhha`   |
| CrackTree | `--dataset cracktree` |

Select a dataset using the `--dataset` flag:
```sh
python -m main -c resunet.yml --dataset deepcrack
```

### Dataset Structure

Datasets should follow this directory layout:
```text
data/{dataset_name}/
├── Train/
│   ├── img/      # input images
│   └── anno/     # binary masks
├── Val/
│   ├── img/
│   └── anno/
└── Test/
    ├── img/
    └── anno/
```
- For more details, see the [Dataset Guide](datasets/DATASET.md)

### Program Output

When you run training, CrackSeg automatically creates an organized output directory:
```text
logs/{model_name}_{dataset_name}/{timestamp}/
├── training.log              # Complete training logs with metrics per epoch
├── best_{model_name}.pth     # Best model checkpoint (highest validation Dice score)
└── img/                      # Visualization outputs (if save_img=True)
    ├── {image_name}_input.jpg    # Original input image
    ├── {image_name}_gt.jpg       # Ground truth mask
    └── {image_name}_pred.jpg     # Predicted mask (white=correct, red=false positive, blue=false negative)
```
**Training Progress:**
- Validation metrics printed at specified intervals
- Automatic best model checkpointing based on Dice score

**Metrics Reported:**
- **Dice Score**: Harmonic mean of precision and recall (primary metric)
- **Precision**: Accuracy of predicted crack pixels
- **Recall**: Coverage of actual crack pixels
- **IoU (Intersection over Union)**: Overlap between prediction and ground truth
- **ODS (Optimal Dataset Scale)**: F1-score at the optimal threshold for the entire dataset
- **OIS (Optimal Image Scale)**: Average of per-image optimal F1-scores

### Example Multiscale Prediction
Multiscale models generate side predictions and a final fused output from the original input, shown here alongside the labeled test image used as the ground truth for comparison

## Release History
* 0.1
    * Initial public release

## License
See ``LICENSE`` for more information

GT-CrackSeg

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