https://github.com/ajithvcoder/circle-u-net
Implementation of Circle U-Net , https://www.mdpi.com/1999-4893/14/6/159/htm
https://github.com/ajithvcoder/circle-u-net
circleunet deep-learning segmentation unet
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Implementation of Circle U-Net , https://www.mdpi.com/1999-4893/14/6/159/htm
- Host: GitHub
- URL: https://github.com/ajithvcoder/circle-u-net
- Owner: ajithvcoder
- License: mit
- Created: 2021-07-02T19:10:48.000Z (over 4 years ago)
- Default Branch: main
- Last Pushed: 2021-07-03T06:58:02.000Z (over 4 years ago)
- Last Synced: 2025-01-12T19:30:44.746Z (about 1 year ago)
- Topics: circleunet, deep-learning, segmentation, unet
- Language: Python
- Homepage:
- Size: 146 KB
- Stars: 0
- Watchers: 1
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
## Circle-U-Net
This is implementation of Circle U Net for image segmentation
[Circle-U-Net: An Efficient Architecture for Semantic Segmentation](https://www.mdpi.com/1999-4893/14/6/159/htm) , published in Algorithms Journal 2021
**Dataset** : [ICG - TUGRAZ dataset](http://dronedataset.icg.tugraz.at)
**Installations**
tensorflow-gpu==2.1.4
### Schematic Diagram
### Architecture
### Dataset Tree
```
---dataset
|
|---data--
|
|
|
|------icg_drone
| |
| |-----train_frames--
| | |-----train------
| | | |001.jpg
| | | |002.jpg
| | | |003.jpg
| |-----train_masks---
| | |-----train------
| | | |001.jpg
| | | |002.jpg
| | | |003.jpg
| |
| |-----val_frames----
| | |-----val--------
| | | |055.jpg
| | | |056.jpg
| | | |057.jpg
| |-----val_masks-----
| | |-----val--------
| | | |055.jpg
| | | |056.jpg
| | | |057.jpg
|
|---label_color.txt
```
In ICG semantic drone dataset ,
- "train_frames" could be taken from - semantic_drone_dataset_semantics_v1.1\semantic_drone_dataset\training_set\images
- "train_masks" could be taken from -
semantic_drone_dataset_semantics_v1.1\semantic_drone_dataset\training_set\gt\semantic\label_images
Please randomly seperate train and val set as you like make sure there are 360 for training and 40 for testing
**Train frames**
all other images except in val set
**Train masks**
all other images except in val set
**Val frames**
3,19,53,71,89,104,122,139,182,177,216,225,244,263,290,304,320,332,367,386
412,421,438,476,489,507,524,545,567,583,584,585,586,587,588,590,591,592,593,593,594
**Val masks**
3,19,53,71,89,104,122,139,182,177,216,225,244,263,290,304,320,332,367,386
412,421,438,476,489,507,524,545,567,583,584,585,586,587,588,590,591,592,593,593,594
### Training
**Attenation Unet**
``` python drone_main.py -d "camvid_small" -idir "dataset/icg_drone/data/" -m "att_unet" -ht 256 -w 256 -bs 5 --loss tversky --num_epochs 60 ```
**Resnet101 Unet**
```python drone_main.py -d "camvid_small" -idir "dataset/icg_drone/data/" -m "res_unet" -ht 256 -w 256 -bs 5 --loss tversky --num_epochs 60 ```
**Unet**
```python drone_main.py -d "camvid_small" -idir "dataset/icg_drone/data/" -m "tiny_unet" -ht 256 -w 256 -bs 5 --loss tversky --num_epochs 60```
**Circlenet** - Tversky loss
```python drone_main.py -d "camvid_small" -idir "dataset/icg_drone/data/" -m "circlenet" -ht 256 -w 256 -bs 5 --loss tversky --num_epochs 60```
**Circlenet** - Categorical cross entropy
```python drone_main.py -d "camvid_small" -idir "dataset/icg_drone/data/" -m "circlenet" -ht 256 -w 256 -bs 5 --loss CCE --num_epochs 60```
**Circlenet with attention** - Tversky loss
```python drone_main.py -d "camvid_small" -idir "dataset/icg_drone/data/" -m "circle_att_101" -ht 256 -w 256 -bs 5 --loss tversky --num_epochs 60 ```
**Circlenet with attention** - Categorical cross entropy
```python drone_main.py -d "camvid_small" -idir "dataset/icg_drone/data/" -m "circle_att_101" -ht 256 -w 256 -bs 5 --loss CCE --num_epochs 60 ```
**Attention unet** - Categorical cross entropy
```python drone_main.py -d "camvid_small" -idir "dataset/icg_drone/data/" -m att_unet -ht 256 -w 256 -bs 5 --loss CCE --num_epochs 60 ```
**Resunet** - Categorical cross entropy
``` python drone_main.py -d "camvid_small" -idir "dataset/icg_drone/data/" -m "res_unet" -ht 256 -w 256 -bs 5 --loss CCE --num_epochs 60 ```
**Squeezeunet** -CCE
``` python drone_main.py -d "camvid_small" -idir "dataset/icg_drone/data/" -m new_squeezenet -ht 256 -w 256 -bs 5 --loss CCE --num_epochs 60 ```
**Evaluating model and predicting images**
python evaluate.py -d "camvid" -idir "dataset/camvid/data/" -mt "squeeze_unet_keras" -m "camvid_model_5_epochs.h5" -ht 256 -w 256
### Related papers :
- [Growth of Unet](https://paperswithcode.com/method/u-net)
- [Unet - root](https://arxiv.org/pdf/1505.04597.pdf)
Last two years SOTA papers
- [UNet](https://arxiv.org/pdf/1505.04597.pdf)
- [UNet++](https://arxiv.org/pdf/1807.10165.pdf)
- [Att_UNet](https://arxiv.org/pdf/1804.03999.pdf)
- [ResUNet](https://arxiv.org/pdf/1512.03385.pdf)
- [RexUnet](https://arxiv.org/pdf/1611.05431.pdf)
- [Adversarial Learning](https://arxiv.org/pdf/1802.07934.pdf)
- [NAS-Unet](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8681706)
CVPR
- [Eff-UNet](https://openaccess.thecvf.com/content_CVPRW_2020/papers/w22/Baheti_Eff-UNet_A_Novel_Architecture_for_Semantic_Segmentation_in_Unstructured_Environment_CVPRW_2020_paper.pdf)
- [Feedback U-net](https://openaccess.thecvf.com/content_CVPRW_2020/papers/w57/Shibuya_Feedback_U-Net_for_Cell_Image_Segmentation_CVPRW_2020_paper.pdf)
- [Enhanced rotation equivariant Unet](https://openaccess.thecvf.com/content_CVPRW_2019/papers/CVMI/Chidester_Enhanced_Rotation-Equivariant_U-Net_for_Nuclear_Segmentation_CVPRW_2019_paper.pdf)
ICCV
- [Reccurent Unets](https://openaccess.thecvf.com/content_ICCV_2019/html/Wang_Recurrent_U-Net_for_Resource-Constrained_Segmentation_ICCV_2019_paper.html)
- [BCDU-Net](http://openaccess.thecvf.com/content_ICCVW_2019/papers/VRMI/Azad_Bi-Directional_ConvLSTM_U-Net_with_Densley_Connected_Convolutions_ICCVW_2019_paper.pdf) - https://github.com/rezazad68/BCDU-Net
- [GAN based_on UNet](https://openaccess.thecvf.com/content_CVPR_2020/papers/Schonfeld_A_U-Net_Based_Discriminator_for_Generative_Adversarial_Networks_CVPR_2020_paper.pdf)
- [RUNet for super resolution](https://openaccess.thecvf.com/content_CVPRW_2019/papers/WiCV/Hu_RUNet_A_Robust_UNet_Architecture_for_Image_Super-Resolution_CVPRW_2019_paper.pdf)
### Cite
Please site our paper if you use this code in your own work:
```
@inproceedings{fengsun2021circleunet,
title={Circle-U-Net: An Efficient Architecture for Semantic Segmentation},
author={Feng Sun, Ajith Kumar V, Guanci Yang, Ansi Zhang, Yiyun Zhang},
booktitle={Algorithms},
year={2021}
}
```
```
Sun F, Yang G, Zhang A, et al. Circle-U-Net: An Efficient Architecture for Semantic Segmentation[J]. Algorithms, 2021, 14(6): 159.
```