This is a repository with code used in "Machine learning for expert-level image-based identification of very similar species in the hyperdiverse plant bug family Miridae (Hemiptera: Heteroptera)" article.
The model training experiments were based on PyTorch neural networks framework.

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Most of essential parameters are placed in config.py. For example:

• DATASETS_LIST: the list of defined datasets for training experiments.

• MODELS_LIST: list of possible models for training.

Available models out-of-the-box from the repo:

['resnet18', 'resnet34', 'resnet50', 'resnet101', 'resnet152', 'wide_resnet50_2', 'wide_resnet101_2', 'resnext50_32x4d', 'resnext101_32x8d', 'mobilenet_v2', 'mobilenet_v3_large' ]

• IMBALANCED_TOOL_LIST: list of possible tools for class-imbalance problem.

Available tools out-of-the-box: ['weighted_loss', 'train_sampler', 'default']

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You can learn about data preparation, model training and testing in separate notebooks:

prepare_dataset.ipynb for dataset preparation.

model_train.ipynb for model training.

model_test.ipynb for model testing.

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It is recommended to configure and use model_training.py, models_test.py separately or the whole process in main.py

You can also revise your trained models with interpretability methods during tests which was added here: Grad-CAM, LIME, RISE:

Toy examples:

Citation

@article{popkov2022machine,
  title={Machine learning for expert-level image-based identification of very similar species in the hyperdiverse plant bug family Miridae (Hemiptera: Heteroptera)},
  author={Popkov, Alexander and Konstantinov, Fedor and Neimorovets, Vladimir and Solodovnikov, Alexey},
  journal={Systematic Entomology},
  publisher={Wiley Online Library}
}

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