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- Deep Neural Network (DNN) Name - for example, VGG16, ResNet-101, UNet, etc., or a link to GitHub repository or manuscript for customized DNNs if applicable.
- Data Augmentation Methods - for example, color augmentation (HSV or RGB color space), transformation, noise, GAN, patch generation, downsizing parameters, etc.
- Training, Validation, and Testing Set Configuration - for example number of samples per each set, total number of samples, etc.
- Hyperparameters - for example, learning rate, early stopping, batch size, number of epochs, etc.
- Training Statistics - for example, wall time spent in training, accuracy metrics such as if average score or best score is reported, etc.
- Training Environment - for example, GPU type, Deep Learning framework used such as TensorFlow/PyTorch, number of GPUs, number of nodes, etc
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