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--- |
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license: mit |
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language: |
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- en |
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pipeline_tag: image-segmentation |
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--- |
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WRU-Net is a segmentation model based on UNet for the semantic segmentation of wheat yellow/stripe rust disease images to segment out rust and non-rust pixels using supervised deep learning. |
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# The NWRD Dataset: An Open-Source Annotated Segmentation Dataset of Diseased Wheat Crop |
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This repo contains the source code for the study presented in the work: |
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`The NWRD Dataset: An Open-Source Annotated Segmentation Dataset of Diseased Wheat Crop`. |
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## Dataset |
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The NWRD dataset is a real-world segmentation dataset of wheat rust diseased and healthy leaf images specifically constructed for semantic segmentation of wheat rust disease. |
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The NWRD dataset consists of 100 images in total at this moment. |
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Sample images from The NWRD dataset; annotated images showing rust disease along with their binary masks: |
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Dataset is available at: https://dll.seecs.nust.edu.pk/downloads/ |
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### Directory Structure |
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The NWRD dataset images are available in `.jpg` format and the annotated binary masks are available in `.png` format. Below is the directory structure of this dataset: |
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``` |
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NWRD |
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├── test |
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│ ├── images |
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│ └── masks |
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└── train |
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├── images |
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└── masks |
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``` |
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### Data Splits |
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Here are the data splits of the NWRD dataset: |
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| Split | Percentage | |
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|-----------|-----------| |
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| Train + Valid | 90 | |
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| Test | 10 | |
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| Total | 100 | |
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The experimentation with 22 images was done with the following set of images: |
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| Split | Images | |
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|-----------|-----------| |
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| Train + Valid | 2, 7, 14, 30, 64, 83, 84, 90, 94, 95, 100, 118, 124, 125, 132, 133, 136, 137, 138, 146 | |
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| Test | 67, 123 | |
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## Requirements |
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* Python |
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* Pytorch |
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* Torchvision |
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* Numpy |
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* Tqdm |
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* Tensorboard |
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* Scikit-learn |
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* Pandas |
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## Usage |
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Different aspects of the training can be tunned from the `config.json` file. |
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Use `python train.py -c config.json` to run code. |
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### Config file format |
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Config files are in `.json` format: |
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```javascript |
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{ |
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"name": "WRS", // training session name |
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"n_gpu": 1, // number of GPUs to use for training. |
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"arch": { |
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"type": "UNet", // name of model architecture to train |
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"args": { |
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"n_channels": 3, |
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"n_classes": 2 |
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} // pass arguments to the model |
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}, |
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"data_loader": { |
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"type": "PatchedDataLoader", // selecting data loader |
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"args":{ |
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"data_dir": "data/", // dataset path |
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"patch_size": 128, // patch size |
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"batch_size": 64, // batch size |
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"patch_stride": 32, // patch overlapping stride |
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"target_dist": 0.01, // least percentage of rust pixels in a patch |
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"shuffle": true, // shuffle training data before |
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"validation_split": 0.1, // size of validation dataset. float(portion) or int(number of samples) |
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"num_workers": 2 // number of cpu processes to be used for data loading |
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} |
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}, |
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"optimizer": { |
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"type": "RMSprop", |
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"args":{ |
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"lr": 1e-6, // learning rate |
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"weight_decay": 0 |
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} |
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}, |
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"loss": "focal_loss", // loss function |
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"metrics": [ // list of metrics to evaluate |
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"precision", |
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"recall", |
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"f1_score" |
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], |
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"lr_scheduler": { |
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"type": "ExponentialLR", // learning rate scheduler |
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"args": { |
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"gamma": 0.998 |
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} |
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}, |
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"trainer": { |
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"epochs": 500, // number of training epochs |
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"adaptive_step": 5, // update dataset after every adaptive_step epochs |
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"save_dir": "saved/", |
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"save_period": 1, // save checkpoints every save_period epochs |
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"verbosity": 2, // 0: quiet, 1: per epoch, 2: full |
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"monitor": "min val_loss", // mode and metric for model performance monitoring. set 'off' to disable. |
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"early_stop": 50, // early stop |
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"tensorboard": true // enable tensorboard visualization |
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} |
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} |
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``` |
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### Using config files |
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Modify the configurations in `.json` config files, then run: |
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``` |
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python train.py --config config.json |
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``` |
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### Resuming from checkpoints |
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You can resume from a previously saved checkpoint by: |
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``` |
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python train.py --resume path/to/checkpoint |
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``` |
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### Using Multiple GPU |
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You can enable multi-GPU training by setting `n_gpu` argument of the config file to larger number. |
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If configured to use smaller number of gpu than available, first n devices will be used by default. |
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Specify indices of available GPUs by cuda environmental variable. |
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``` |
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python train.py --device 2,3 -c config.json |
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``` |
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This is equivalent to |
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``` |
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CUDA_VISIBLE_DEVICES=2,3 python train.py -c config.py |
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``` |
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## License |
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This project is licensed under the MIT License. See [LICENSE](LICENSE) for more details |
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## Acknowledgements |
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* This research project was funded by German Academic Exchange Service (DAAD). |
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* This project follows the template provided by [victoresque](https://github.com/victoresque/pytorch-template) |
