leaf-segmentation-unet

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leaf-segmentation-unet

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a unet model trained for the semantic segmentation of leaf images 🍃

leaf-segmentation-unet 최신버젼 다운로드

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// repository documentation

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# leaf-segmentation-unet
<p>
    <img src="https://img.shields.io/github/stars/gvil-research/leaf-segmentation-unet?style=flat-square"/>
    <img src="https://img.shields.io/github/last-commit/gvil-research/leaf-segmentation-unet?style=flat-square"/>
</p>

a unet model trained for the semantic segmentation of leaf images

## folder structure

```
.
├── dataset
│   └── note.md
├── model
│   ├── pretrained
│   │   └── download.md
│   ├── __init__.py
│   └── model.py
├── preprocess
│   ├── generate_dataset.py
│   ├── __init__.py
│   └── README.md
├── test
│   └── get_testset.sh
├── utilities
│   ├── __init__.py
│   └── utility.py
├── get_dataset.sh
├── get_pretrained.sh
├── LICENSE
├── predict.py
├── README.md
├── requirements.txt
└── train.py
```
#### output samples
![1](https://raw.githubusercontent.com/gvil-research/leaf-segmentation-unet/main/static/1.png)
![2](https://raw.githubusercontent.com/gvil-research/leaf-segmentation-unet/main/static/2.png)
![3](https://raw.githubusercontent.com/gvil-research/leaf-segmentation-unet/main/static/3.png)
> input images are from the dataset of [Plant Pathology 2021 Challenge](https://www.kaggle.com/c/plant-pathology-2021-fgvc8)

## instructions

### train from scratch

* prepare dataset

linux users can run `get_dataset.sh` instead of first three steps
    1. cd into `./preprocess`
    1. download `DenseLeaves.zip` from [here](https://www.egr.msu.edu/denseleaves/Data/DenseLeaves.zip)
    1. unzip the downloaded file as `./preprocess/DenseLeaves/`

1. run `python generate_dataset.py`

The newly processed dataset is now saved at `./dataset`

* cd back to project root and run `python train.py` to train the model

When training, the model saves the weights in the `./model/pretrained` model.
* `latest_weights.pth` - weights saved at the end of the last epoch
* `best_val_weights.pth` - weights saved when the model obtained minimum validation loss

### use pretrained (prediction)

* download pretrained weights

linux users can run `get_pretrained.sh` (make sure `gdown` is installed -- `pip install gdown`)

others can download the weights from links provided in this [file](./model/pretrained/download.md)

* specify test image location

edit the `TEST_DIR` variable in `predict.py` to specify custom images or download a sample dataset by running `get_testset.sh` in `./test` folder.

> **tip**: if the segmentation results are not satisfactory, modify the mask threshold values in `predict.py` file
### acknowledgement

* DenseLeaves dataset - Michigan State University [visit](https://www.egr.msu.edu/denseleaves/)
* Plant Pathology 2021 dataset - Kaggle

### update log
2021-06-24: first code upload, most of the code is really bad (I wrote them a while ago). I shall refactor them soon.

leaf-segmentation-unet

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