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keras-swin-unet

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Revolutionize geospatial analysis with Swin-UNet – a cutting-edge solution for satellite imagery segmentation using Swin Transformers and UNet. Achieve SOTA precision in road extraction, ideal for GIS professionals, urban planners, and AI researchers. Train, test, and infer with just three lines of code.

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# Swin-UNet — Satellite Imagery Segmentation with Transformers

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**Revolutionize geospatial analysis with Swin-UNet** — a cutting-edge solution for satellite imagery segmentation using Swin Transformers and UNet. Achieve state-of-the-art precision in road extraction, land cover classification, and infrastructure mapping. Ideal for GIS professionals, urban planners, and AI researchers. Train, test, and infer with just three lines of code.

```python
from keras_swin_unet import swin_train, swin_infer

swin_train(data="./dataset", epochs=50)           # train
swin_infer(image="input.jpg", output="out.png")   # predict
```

Swin-UNet combines the **Swin Transformer** (hierarchical shifted-window self-attention) with the **U-Net** encoder-decoder design. It captures global context that CNNs miss while remaining computationally efficient. This package wraps the full architecture into a production-ready Keras pipeline — training, validation, inference, and visualization in a single call.

## Installation

```bash
pip install keras-swin-unet
```

Requires Python 3.10+ and TensorFlow 2.19+.

## Quick Start

### 1. Prepare Your Data

```
dataset/
  images/    # RGB images (.png, .jpg, .tif, .npy)
  masks/     # Pixel-level labels (same filenames, any image format)
```

For binary segmentation, masks should have pixel values `0` (background) and `255` (foreground). For multi-class, use values `0, 1, 2, ...` and set `num_classes` accordingly.

### 2. Train

```python
from keras_swin_unet import swin_train

swin_train(
    data="./dataset",
    model_dir="./checkpoint",
    num_classes=2,
    epochs=50,
    bs=8,
    loss_type="focal_dice",
    input_shape=[256, 256, 3],
)
```

That's it. The training loop handles data splitting (80/10/10), normalization, augmentation-ready loading, focal loss with class balancing, early stopping, model checkpointing, and saves evaluation metrics as JSON.

### 3. Predict

```python
from keras_swin_unet import swin_infer

# Batch inference on a test set
swin_infer(data="./dataset", model_dir="./checkpoint")

# Single image
swin_infer(image="photo.jpg", output="segmentation.png", model_dir="./checkpoint")
```

### CLI (Alternative)

```bash
swin-unet train --data ./dataset --epochs 50 --loss-type focal_dice
swin-unet infer --image photo.jpg --output result.png --model-dir ./checkpoint
```

## Loss Functions

Choose the loss that fits your data. Change it with `loss_type="..."`.

| `loss_type` | Behavior | Best For |
|-------------|----------|----------|
| `focal` | Down-weights easy examples | Heavy class imbalance |
| `dice` | Optimizes region overlap directly | IoU-critical tasks |
| `bce` | Standard binary cross-entropy | Balanced classes |
| `bce_dice` | Pixel loss + region loss | General purpose |
| `focal_dice` | Focal + Dice combined | Imbalanced + precision boundary |
| `focal_tversky` | Focal applied to Tversky | Asymmetric FP/FN costs |
| `tversky` | Weighted Dice variant | Tunable precision/recall trade-off |

All losses are proper Keras `Loss` subclasses — models save and load with `model.save()` / `load_model()` without custom objects for the loss.

## API

### `swin_train(**kwargs)`

Only `data` is required. Everything else has sensible defaults.

| Parameter | Default | Description |
|-----------|---------|-------------|
| `data` | `"./demo_data"` | Dataset directory with `images/` and `masks/` |
| `model_dir` | `"./checkpoint"` | Where to save model and metrics |
| `num_classes` | `2` | Number of output classes |
| `epochs` | `5` | Training epochs |
| `bs` | `4` | Batch size |
| `patience` | `3` | EarlyStopping patience |
| `filter` | `128` | Embedding dimension (model capacity) |
| `depth` | `4` | Encoder/decoder stages |
| `stack_down` | `2` | Swin blocks per encoder stage |
| `stack_up` | `2` | Swin blocks per decoder stage |
| `patch_size` | `[4, 4]` | Initial patch size |
| `num_heads` | `[4, 8, 8, 8]` | Attention heads per stage |
| `window_size` | `[4, 2, 2, 2]` | Attention window per stage |
| `num_mlp` | `512` | MLP hidden size in Swin blocks |
| `loss_type` | `"focal"` | Loss function (see table above) |
| `gamma` | `2.0` | Gamma for focal-based losses |
| `alpha` | `0.25` | Alpha for focal-based losses |
| `input_shape` | `[512, 512, 3]` | Input image shape [H, W, C] |
| `input_scale` | `255` | Divide input images by this |
| `mask_scale` | `255` | Divide mask images by this |
| `visualize` | `2` | How many test images to visualize |

### `swin_infer(**kwargs)`

| Parameter | Default | Description |
|-----------|---------|-------------|
| `model_dir` | `"./checkpoint"` | Path to trained model |
| `image` | `None` | Single image to segment (if `None`, runs on test set) |
| `output` | `"output.png"` | Where to save the overlay |
| `data` | `"./demo_data"` | Dataset for batch inference |
| `num_classes` | `2` | Must match training |
| `input_shape` | `[512, 512, 3]` | Must match training |
| `input_scale` | `255` | Must match training |
| `mask_scale` | `255` | Must match training |
| `visualize` | `1` | How many samples to visualize |

## Architecture

```
Input [H, W, 3]
  → Patch Embedding (4×4) → [H/4, W/4, C]
  → Encoder Stage 0: Swin Blocks → skip₀
  → Patch Merging → [H/8, W/8, 2C]
  → Encoder Stage 1: Swin Blocks → skip₁
  → Patch Merging → [H/16, W/16, 4C]
  → Encoder Stage 2: Swin Blocks → skip₂
  → Patch Merging → [H/32, W/32, 8C]
  → Encoder Stage 3: Swin Blocks → skip₃
  → Bottleneck: Swin Blocks
  → Patch Expanding → concat skip₂ → Decoder Stage 0
  → Patch Expanding → concat skip₁ → Decoder Stage 1
  → Patch Expanding → concat skip₀ → Decoder Stage 2
  → Patch Expanding (4×) → pixel resolution
  → Conv2D Softmax → Segmentation Map
```

Each Swin Transformer block alternates **regular** and **shifted-window** multi-head self-attention. Shifted windows allow cross-window communication without the quadratic cost of global attention.

## Advanced Usage

For full control, build the model directly:

```python
from swin_transformer.model_loader import get_model
from swin_transformer.AUC_LOSS import FocalDiceLoss

model = get_model(
    input_size=(512, 512, 3),
    filter_num_begin=96,
    depth=4,
    stack_num_down=2,
    stack_num_up=2,
    patch_size=[4, 4],
    num_heads=[3, 6, 12, 24],
    window_size=[7, 7, 7, 7],
    num_mlp=1024,
    num_classes=5,           # multi-class segmentation
)
model.compile(
    optimizer="adam",
    loss=FocalDiceLoss(alpha=0.25, gamma=2.0, focal_weight=0.5),
    metrics=["accuracy"],
)
model.fit(train_loader, validation_data=val_loader, epochs=100)
```

## Performance

### PennFudan Pedestrian Detection

Binary segmentation, 256×256, **170 images, trained from scratch** on CPU. No pre-training, no fine-tuning. 63 epochs, 11.4M params.

| Metric | Score |
|--------|-------|
| Accuracy | 85.3% |
| IoU (Jaccard) | 0.641 |
| Precision | 77.8% |
| Recall | 75.3% |
| F1 Score | 0.764 |

**Best prediction** from the test set (IoU 0.813, Dice 0.897):

![PennFudan Showcase](https://raw.githubusercontent.com/L-A-Sandhu/keras-swin-unet/main/Results/pennfudan/showcase.png)

*Left to right: input image, ground truth mask, model prediction, error map (green = true positive, red = false positive, blue = false negative).*

### Multi-Sample Comparison

![PennFudan Grid](https://raw.githubusercontent.com/L-A-Sandhu/keras-swin-unet/main/Results/pennfudan/comparison_grid.png)

*Three test samples with per-sample IoU/Dice scores. The model segments pedestrians accurately despite heavy class imbalance (~10% foreground pixels) and only 118 training images.*

## Applications

- **Remote Sensing & GIS** — road extraction, land cover classification, building detection
- **Medical Imaging** — organ segmentation, tumor delineation, cell counting
- **Autonomous Driving** — lane marking, pedestrian detection, drivable area
- **Agriculture** — crop field boundaries, vegetation indices
- **Industrial** — defect detection, part segmentation, quality control

## Citation

If you use this package in your research, please cite the original works:

```bibtex
@inproceedings{cao2022swinunet,
  title={Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation},
  author={Cao, Hu and Wang, Yueyue and Chen, Joy and Jiang, Dongsheng and
          Zhang, Xiaopeng and Tian, Qi and Wang, Manning},
  booktitle={ECCV Workshops},
  year={2022}
}

@inproceedings{liu2021swin,
  title={Swin Transformer: Hierarchical Vision Transformer using Shifted Windows},
  author={Liu, Ze and Lin, Yutong and Cao, Yue and Hu, Han and Wei, Yixuan and
          Zhang, Zheng and Lin, Stephen and Guo, Baining},
  booktitle={ICCV},
  year={2021}
}
```

## License

MIT.

---

**Maintainer**: Laeeq Aslam — [laeeq.aslam.100@gmail.com](mailto:laeeq.aslam.100@gmail.com)

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