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A Tensorflow implementation of Unet

U-net 최신버젼 다운로드

최종 버전 다운로드 (.zip)

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# Unet for image segmentation 
My project is inspired by original architecture: [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://arxiv.org/abs/1505.04597) 
## Binary semantic segmentation 
### <a href = 'https://www.kaggle.com/maedemaftouni/covid19-ct-scan-lesion-segmentation-dataset'>COVID-19 CT scan lesion segmentation</a>
|![image](https://raw.githubusercontent.com/Nguyendat-bit/U-net/main/img/origins.gif)|![image](https://raw.githubusercontent.com/Nguyendat-bit/U-net/main/img/groundtruth.gif)|![image](https://raw.githubusercontent.com/Nguyendat-bit/U-net/main/img/prediction.gif)|
|:---:|:---:|:---:|
| Original | Ground truth | Prediction |

Review training on colab:

- Unet -->  [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1hb_W3pPkB8Fs_n9c4Z6zyuL_yQkNHmOH?usp=sharing)
- Mobilenetv2 - Unet --> [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/12oJvjjdJIXN1YAySF1LhfFgJ3dG0uHHT?usp=sharing) 
- Resnet50 - Unet --> [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1ROfmqetdu0Z4bRTRpH3dRJeH-FIFpcfT?usp=sharing)

### <a href = 'https://www.kaggle.com/dat0chin/electron-microscopy-dataset'>Mitochondria Segmentation</a>
<img src = 'img/mitochondria.png'>

Review training on colab:

[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1Dct1nq5qa-iwCNnp1z50zXquJBJOM9JV?usp=sharing)

## Multiclass semantic segmentation with Resnet50-Unet (<a href = 'https://www.kaggle.com/intelecai/car-segmentation'>Car Segmentation</a>)
<img src = 'img/Car_segmentation.png'>

Review training on kaggle:

[![Kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://www.kaggle.com/dat0chin/car-segmentation-with-resnet50-unet)

## Overview
In this my model, I will resize `INPUT_SHAPE` and `OUTPUT_SHAPE` equally. The reason is that when the dimensions are equal, the model will not lose position and spatial information. Some layers of the SOTA model module have an image mask that is resized to the same size as the input. 
To do so, I use `padding = same` and `LeakyRelu` as an alternative to `Relu`

In addition, I have implemented Unet models with the encoding as the `Mobilenetv2 and Resnet50 backbones`
### Original Unet Architecture
 
<img src = 'img/Unet.png' , alt = 'Unet',style="width:800px;height:600px">

### Author
<ul>
 <li>Github: <a href = "https://github.com/Nguyendat-bit">Nguyendat-bit</a> </li>
 <li>Email: <a href = "nduc0231@gmai.com">nduc0231@gmail</a></li>
 <li>Linkedin: <a href = "https://www.linkedin.com/in/nguyendat4801">Đạt Nguyễn Tiến</a></li>
</ul>

## Usage
### Dependencies
- python >= 3.9
- numpy >= 1.20.3
- tensorflow >= 2.7.0
- opencv >= 4.5.4
- matplotlib >= 3.4.3

### Train your model by running this command line

Training script:

```python

python train.py --all-train ${link_to_train_origin_folder} --all-train ${link_to_train_mask_folder} --epochs ${epochs}

```

Example:

```python

python train.py  --all-train frames/*.jpg --all-train masks/*.jpg --batch-size 4 --classes 2 --epochs 2 --color-mode gray --use-kmean True --image-size 64

```

There are some important arguments for the script you should consider when running it:

- `all-train`: The folder of training data 
- `all-valid`: The folder of validation data
- `color-mode`: hsv or rgb or gray
- `lr`: The learning rate
- `image-size`: The image size of the dataset
- `model-save`: Where the model after training saved

## Feedback
If you meet any issues when using this library, please let us know via the issues submission tab.

U-net

U-net 최신버젼 다운로드

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U-net 최신버젼 다운로드

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