# Kvasir-Sessile Dataset (Kvasir SEG)

## Description

This project supports **`Kvasir-Sessile Dataset (Kvasir SEG) `**, which can be downloaded from [here](https://opendatalab.com/Kvasir-Sessile_dataset).

## Dataset Overview

The Kvasir-SEG dataset contains polyp images and their corresponding ground truth from the Kvasir Dataset v2. The resolution of the images contained in Kvasir-SEG varies from 332x487 to 1920x1072 pixels.

<!-- For a typical model, this section should contain the commands for training and testing. You are also suggested to dump your environment specification to env.yml by `conda env export > env.yml`. -->

### Information Statistics

| Dataset Name                                                  | Anatomical Region | Task Type    | Modality  | Num. Classes | Train/Val/Test Images | Train/Val/Test Labeled | Release Date | License                                                   |
| ------------------------------------------------------------- | ----------------- | ------------ | --------- | ------------ | --------------------- | ---------------------- | ------------ | --------------------------------------------------------- |
| [Kvarsir-SEG](https://opendatalab.com/Kvasir-Sessile_dataset) | abdomen           | segmentation | endoscopy | 2            | 196/-/-               | yes/-/-                | 2020         | [CC-BY 4.0](https://creativecommons.org/licenses/by/4.0/) |

| Class Name | Num. Train | Pct. Train | Num. Val | Pct. Val | Num. Test | Pct. Test |
| :--------: | :--------: | :--------: | :------: | :------: | :-------: | :-------: |
| background |    196     |   92.31    |    -     |    -     |     -     |     -     |
|   polyp    |    196     |    7.69    |    -     |    -     |     -     |     -     |

Note:

- `Pct` means percentage of pixels in this category in all pixels.

### Visualization

![kvasir-seg](https://raw.githubusercontent.com/uni-medical/medical-datasets-visualization/main/2d/semantic_seg/endoscopy_images/kvasir_seg/kvasir_seg_dataset.png?raw=true)

### Dataset Citation

```
@inproceedings{jha2020kvasir,
	title={Kvasir-seg: A segmented polyp dataset},
	author={Jha, Debesh and Smedsrud, Pia H and Riegler, Michael A and Halvorsen, P{\aa}l and Lange, Thomas de and Johansen, Dag and Johansen, H{\aa}vard D},
	booktitle={International Conference on Multimedia Modeling},
	pages={451--462},
	year={2020},
	organization={Springer}
 }
```

### Prerequisites

- Python v3.8
- PyTorch v1.10.0
- pillow(PIL) v9.3.0
- scikit-learn(sklearn) v1.2.0
- [MIM](https://github.com/open-mmlab/mim) v0.3.4
- [MMCV](https://github.com/open-mmlab/mmcv) v2.0.0rc4
- [MMEngine](https://github.com/open-mmlab/mmengine) v0.2.0 or higher
- [MMSegmentation](https://github.com/open-mmlab/mmsegmentation) v1.0.0rc5

All the commands below rely on the correct configuration of `PYTHONPATH`, which should point to the project's directory so that Python can locate the module files. In `kvasir_seg/` root directory, run the following line to add the current directory to `PYTHONPATH`:

```shell
export PYTHONPATH=`pwd`:$PYTHONPATH
```

### Dataset Preparing

- download dataset from [here](https://opendatalab.com/Kvasir-Sessile_dataset) and decompress data to path `'data/'`.
- run script `"python tools/prepare_dataset.py"` to format data and change folder structure as below.
- run script `"python ../../tools/split_seg_dataset.py"` to split dataset and generate `train.txt`, `val.txt` and `test.txt`. If the label of official validation set and test set cannot be obtained, we generate `train.txt` and `val.txt` from the training set randomly.

```none
  mmsegmentation
  ├── mmseg
  ├── projects
  │   ├── medical
  │   │   ├── 2d_image
  │   │   │   ├── endoscopy
  │   │   │   │   ├── kvasir_seg
  │   │   │   │   │   ├── configs
  │   │   │   │   │   ├── datasets
  │   │   │   │   │   ├── tools
  │   │   │   │   │   ├── data
  │   │   │   │   │   │   ├── train.txt
  │   │   │   │   │   │   ├── val.txt
  │   │   │   │   │   │   ├── images
  │   │   │   │   │   │   │   ├── train
  │   │   │   │   |   │   │   │   ├── xxx.png
  │   │   │   │   |   │   │   │   ├── ...
  │   │   │   │   |   │   │   │   └── xxx.png
  │   │   │   │   │   │   ├── masks
  │   │   │   │   │   │   │   ├── train
  │   │   │   │   |   │   │   │   ├── xxx.png
  │   │   │   │   |   │   │   │   ├── ...
  │   │   │   │   |   │   │   │   └── xxx.png
```

### Divided Dataset Information

***Note: The table information below is divided by ourselves.***

| Class Name | Num. Train | Pct. Train | Num. Val | Pct. Val | Num. Test | Pct. Test |
| :--------: | :--------: | :--------: | :------: | :------: | :-------: | :-------: |
| background |    156     |   92.28    |    40    |  92.41   |     -     |     -     |
|   polyp    |    156     |    7.72    |    40    |   7.59   |     -     |     -     |

### Training commands

To train models on a single server with one GPU. (default)

```shell
mim train mmseg .configs/${CONFIG_FILE}
```

### Testing commands

To test models on a single server with one GPU. (default)

```shell
mim test mmseg ./configs/${CONFIG_FILE}  --checkpoint ${CHECKPOINT_PATH}
```

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You should claim whether this is based on the pre-trained weights, which are converted from the official release; or it's a reproduced result obtained from retraining the model in this project. -->

## Checklist

- [x] Milestone 1: PR-ready, and acceptable to be one of the `projects/`.

  - [x] Finish the code
  - [x] Basic docstrings & proper citation
  - [ ] Test-time correctness
  - [x] A full README

- [x] Milestone 2: Indicates a successful model implementation.

  - [x] Training-time correctness

- [ ] Milestone 3: Good to be a part of our core package!

  - [ ] Type hints and docstrings
  - [ ] Unit tests
  - [ ] Code polishing
  - [ ] Metafile.yml

- [ ] Move your modules into the core package following the codebase's file hierarchy structure.

- [ ] Refactor your modules into the core package following the codebase's file hierarchy structure.