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--- |
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license: mit |
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language: |
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- en |
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base_model: |
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- google/vit-base-patch16-224 |
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--- |
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# Research paper: Segmentation of Wheat Rust Disease Using Co-Salient Feature Extraction |
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can be accessed via https://doi.org/10.3390/agriengineering7020023 |
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RustCoSeg is a is a novel two-stage pipeline that first classifies wheat leaf images using a Vision Transformer, followed by |
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segmentation using a Co-Salient Object Detection-inspired architecture, effectively isolating rust-infected regions across related samples. |
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Base models include: |
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--ViT based classification and DCFM based co-salient feature extraction for segmenting out wheat rust disease. |
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# ViT |
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Vision Transformer. |
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# DCFM |
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The official repo of the paper `Democracy Does Matter: Comprehensive Feature Mining for Co-Salient Object Detection`. |
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## Environment Requirement |
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create enviroment and intall as following: |
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`pip install -r requirements.txt` |
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## Data Format |
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trainset: CoCo-SEG |
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testset: CoCA, CoSOD3k, Cosal2015 |
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Put the [CoCo-SEG](https://drive.google.com/file/d/1GbA_WKvJm04Z1tR8pTSzBdYVQ75avg4f/view), [CoCA](http://zhaozhang.net/coca.html), [CoSOD3k](http://dpfan.net/CoSOD3K/) and [Cosal2015](https://drive.google.com/u/0/uc?id=1mmYpGx17t8WocdPcw2WKeuFpz6VHoZ6K&export=download) datasets to `DCFM/data` as the following structure: |
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``` |
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DCFM |
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├── other codes |
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├── ... |
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│ |
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└── data |
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├── CoCo-SEG (CoCo-SEG's image files) |
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├── CoCA (CoCA's image files) |
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├── CoSOD3k (CoSOD3k's image files) |
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└── Cosal2015 (Cosal2015's image files) |
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``` |
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## Trained model |
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trained model can be downloaded from [papermodel](https://drive.google.com/file/d/1cfuq4eJoCwvFR9W1XOJX7Y0ttd8TGjlp/view?usp=sharing). |
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Run `test.py` for inference. |
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The evaluation tool please follow: https://github.com/zzhanghub/eval-co-sod |
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<!-- USAGE EXAMPLES --> |
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## Usage |
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Download pretrainde backbone model [VGG](https://drive.google.com/file/d/1Z1aAYXMyJ6txQ1Z9N7gtxLOIai4dxrXd/view?usp=sharing). |
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Run `train.py` for training. |
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## Prediction results |
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The co-saliency maps of DCFM can be found at [preds](https://drive.google.com/file/d/1wGeNHXFWVSyqvmL4NIUmEFdlHDovEtQR/view?usp=sharing). |
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## Reproduction |
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reproductions by myself on 2080Ti can be found at [reproduction1](https://drive.google.com/file/d/1vovii0RtYR_EC0Y2zxjY_cTWKWM3WaxP/view?usp=sharing) and [reproduction2](https://drive.google.com/file/d/1YPOKZ5kBtmZrCDhHpP3-w1GMVR5BfDoU/view?usp=sharing). |
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reprodution by myself on TITAN X can be found at [reproduction3](https://drive.google.com/file/d/1bnGFtRTYkVXqI2dcjeWFRDXnqqbUUBJr/view?usp=sharing). |
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## Others |
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The code is based on [GCoNet](https://github.com/fanq15/GCoNet). |
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I've added a validation part to help select the model for closer results. This validation part is based on [GCoNet_plus](https://github.com/ZhengPeng7/GCoNet_plus). You can try different evaluation metrics to select the model. |
