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[NeurIPS & MICCAI 2023] Optimization with JDTLoss and Evaluation with Fine-grained Metrics for Semantic Segmentation

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# Optimization with JDTLoss and Evaluation with Fine-grained mIoUs for Semantic Segmentation

## Models
<details>
<summary>Methods</summary>

* [UNet](https://arxiv.org/abs/1505.04597)
* [DeepLabV3](https://arxiv.org/abs/1706.05587)
* [DeepLabV3+](https://arxiv.org/abs/1802.02611)
* [PSPNet](https://arxiv.org/abs/1612.01105)
* [UPerNet](https://arxiv.org/abs/1807.10221)
* [SegFormer](https://arxiv.org/abs/2105.15203)

</details>

<details>
<summary>Backbones</summary>

* [ResNet18/34/50/101/152](https://arxiv.org/abs/1512.03385)
* [ConvNeXt-B](https://arxiv.org/abs/2201.03545)
* [Xception65](https://arxiv.org/abs/1610.02357)
* [EfficientNet-B0](https://arxiv.org/abs/1905.11946)
* [MobileNetV2](https://arxiv.org/abs/1801.04381)
* [MiTB0/B1/B2/B3/B4](https://arxiv.org/abs/2105.15203)
* [MobileViTV2](https://arxiv.org/abs/2206.02680)
* [`timm`](https://github.com/huggingface/pytorch-image-models)

</details>

## Datasets
<details>
<summary>Urban</summary>

* [Cityscapes](https://arxiv.org/abs/1604.01685)
* [Nighttime Driving](https://arxiv.org/abs/1810.02575)
* [Dark Zurich](https://arxiv.org/abs/1901.05946)
* [Mapillary Vistas](https://openaccess.thecvf.com/content_ICCV_2017/papers/Neuhold_The_Mapillary_Vistas_ICCV_2017_paper.pdf)
* [CamVid](https://link.springer.com/chapter/10.1007/978-3-540-88682-2_5)

</details>

<details>
<summary>"Thing" & "Stuff"</summary>

* [ADE20K](https://arxiv.org/abs/1608.05442)
* [COCO-Stuff](https://arxiv.org/abs/1612.03716)
* [PASCAL VOC](https://link.springer.com/article/10.1007/s11263-009-0275-4)
* [PASCAL Context](https://ieeexplore.ieee.org/document/6909514)

</details>

<details>
<summary>Aerial</summary>

* [DeepGlobe Land](https://arxiv.org/abs/1805.06561)
* [DeepGlobe Road](https://arxiv.org/abs/1805.06561)
* [DeepGlobe Building](https://arxiv.org/abs/1805.06561)

</details>

<details>
<summary>Medical</summary>

* [LiTS](https://arxiv.org/abs/1901.04056)
* [KiTS](https://arxiv.org/abs/1904.00445)
* [QUBIQ](https://qubiq.grand-challenge.org)

</details>

## Metrics
<details>
<summary>Accuracy</summary>

* $\text{Acc}$
* $\text{mAcc}^\text{D, I, C}$
* $\text{mIoU}^\text{D, I, C}$
* $\text{mDice}^\text{D, I, C}$
* Worst-case metrics

</details>

<details>
<summary>Calibration Error</summary>

* $\text{ECE}^\text{D, I}$
* $\text{SCE}^\text{D, I}$

</details>

## Prerequisites
#### Requirements
* Hardware: at least 12GB GPU
* Software:
  * [`timm`](https://github.com/huggingface/pytorch-image-models)
  * [`MMSegmentation`](https://github.com/open-mmlab/mmsegmentation) (only for preparing datasets)

#### Prepapre Datasets
* [DATA.md](DATA.md)

## Usage
* To use JDTloss in your codebase
```
from losses.jdt_loss import JDTLoss

# Jaccard loss (default): JDTLoss()
# Dice loss: JDTLoss(alpha=0.5, beta=0.5)
criterion = JDTLoss()

for (image, label) in data_loader:
    logits = model(image)
    loss = criterion(logits, label)
```

* To use fine-grained mIoUs in your codebase
```
from metrics.metric_group import MetricGroup

metric_group = MetricGroup(num_classes=..., ignore_index=...)

for (image, label) in data_loader:
    logits = model(image)
    prob = logits.log_softmax(dim=1).exp()
    # Both `prob` and `label` need to be on CPU
    metric_group.add(prob, label)

results = metric_group.value()
```

* Training with hard labels
```
python main.py \
  --data_dir "path/to/data_dir" \
  --output_dir "path/to/output_dir" \
  --model_yaml "deeplabv3plus_resnet101d" \
  --data_yaml "cityscapes" \
  --label_yaml "hard" \
  --loss_yaml "jaccard_ic_present_all" \
  --schedule_yaml "40k_iters" \
  --optim_yaml "adamw_lr6e-5" \
  --test_yaml "test_iou"
```

* Training with soft labels
  <details>
  <summary>Label Smoothing</summary>

* You may need to tune $\epsilon$ accordingly.
  ```
  python main.py \
    --data_dir "path/to/data_dir" \
    --output_dir "path/to/output_dir" \
    --model_yaml "deeplabv3_resnet50d" \
    --data_yaml "cityscapes" \
    --label_yaml "ls" \
    --loss_yaml "jaccard_d_present_all" \
    --schedule_yaml "40k_iters" \
    --optim_yaml "adamw_lr6e-5" \
    --test_yaml "test_iou"
  ```
  </details>

<details>
  <summary>Knowledge Distillation</summary>

* Step 1: Train a teacher with label smoothing. You are encouraged to repeat the training script at least three times and choose the model with the best performance as the teacher.
  * Step 2: Train a student.
  ```
  python main.py \
    --teacher_checkpoint "path/to/teacher_checkpoint"
    --data_dir "path/to/data_dir" \
    --output_dir "path/to/output_dir" \
    --model_yaml "deeplabv3_resnet18d" \
    --teacher_model_yaml "deeplabv3_resnet50d" \
    --data_yaml "cityscapes" \
    --label_yaml "kd" \
    --loss_yaml "jaccard_d_present_all" \
    --schedule_yaml "40k_iters" \
    --optim_yaml "adamw_lr6e-5" \
    --test_yaml "test_iou"
  ```
  </details>

<details>
  <summary>Multiple Annotators</summary>

```
  python main.py \
    --data_dir "path/to/data_dir" \
    --output_dir "path/to/output_dir" \
    --model_yaml "unet_resnet50d" \
    --data_yaml "qubiq_brain_growth_fold0_task0" \
    --label_yaml "mr" \
    --loss_yaml "jaccard_d_present_all" \
    --schedule_yaml "150_epochs" \
    --optim_yaml "adamw_lr6e-5" \
    --test_yaml "test_iou"
  ```
  </details>

## FAQs
[FAQs.md](FAQs.md)

## Acknowledgements
We express our gratitude to the creators and maintainers of the following projects: [`pytorch-image-models`](https://github.com/huggingface/pytorch-image-models), [`MMSegmentation`](https://github.com/open-mmlab/mmsegmentation), [`segmentation_models.pytorch`](https://github.com/qubvel/segmentation_models.pytorch), [`structure_knowledge_distillation`](https://github.com/irfanICMLL/structure_knowledge_distillation)

## Citations
```BibTeX
@InProceedings{Wang2023Revisiting,
  title     = {Revisiting Evaluation Metrics for Semantic Segmentation: Optimization and Evaluation of Fine-grained Intersection over Union},
  author    = {Wang, Zifu and Berman, Maxim and Rannen-Triki, Amal and Torr, Philip H.S. and Tuia, Devis and Tuytelaars, Tinne and Van Gool, Luc and Yu, Jiaqian and Blaschko, Matthew B.},
  booktitle = {NeurIPS},
  year      = {2023}
}

@InProceedings{Wang2023Jaccard,
  title     = {Jaccard Metric Losses: Optimizing the Jaccard Index with Soft Labels},
  author    = {Wang, Zifu and Ning, Xuefei and Blaschko, Matthew B.},
  booktitle = {NeurIPS},
  year      = {2023}
}

@InProceedings{Wang2023Dice,
  title     = {Dice Semimetric Losses: Optimizing the Dice Score with Soft Labels},
  author    = {Wang, Zifu and Popordanoska, Teodora and Bertels, Jeroen and Lemmens, Robin and Blaschko, Matthew B.},
  booktitle = {MICCAI},
  year      = {2023}
}
```

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