Number of parallel threads: This parameter will distribute micrograph preprocessing over multiple threads to reduce the preprocessing time. Higher values may lead to overhead. Thus values between 4-8 are generally safe. If the preprocessing is observed to still be too slow, higher values can be run.
Degree of lowpass filtering: If micrographs are observed to be too noisy, it is likely that the model may struggle with learning particle locations. Decreasing this parameter can reduce the noise in the micrographs thereby improving training. Values that are too low can begin filtering valuable information from the micrographs. 50 is a standard value and values down to 15 are recommended for noisier micrographs.
Initial learning rate and final learning rate: These two parameters are used to determine the learning decay used in the training. The initial learning rate is expected to be higher than the final learning rate. Values of 0.01 to 0.001 have been found to work best for the initial learning rate. However, it takes a long time to reach training and validation losses of 1 and below, it is recommended to increase the initial learning rate. The final learning rate can be altered to improve the final loss and accuracy. To determine whether an acceptable final learning rate was selected, observe the losses from the final few epochs. If the training loss is significantly lower than the validation loss, this indicates that the final learning rate should be decreased. Indicators that the learning rate is too high include losses and accuracies that do not change and losses that remain large over the initial epochs.
Minibatch size: The minibatch size is used to determine the size of the batches that are fed into the model during training. A minibatch size of 1 has been found to perform for particle picking. Increasing the minibatch size results with faster training at the cost of higher GPU memory cost. It has also been found that the stochasticity introduced by the increased minibatch size can result with worse particle picks.
Number of epochs: The number of epochs is the number of passthroughs through the dataset that is performed during training. It is recommended to first run a training job with a lower number of epochs such as between 20 to 50 and then increase the number of epochs if the loss was continuing to decrease at the end of the training job. If the loss stagnated at the end of training, use a value slightly greater than the epoch at which the loss began to stagnate. For example, if the loss began to stagnate at epoch 10, the next training job can have 15 epochs. The slight increase is to account for the fact that the training will perform less epochs in lower learning rates.
Use class weights: The use class weights parameter alters the loss function to increase the impact of correctly picking particles. For datasets with many particles per micrograph such as the T20S proteasome dataset, it is unnecessary to set this parameter. However, if the particles are sparse in the dataset, setting this parameter on can greatly improve performance. As a rule of thumb, it is suggested to keep this parameter on.
Normalize micrographs: The normalize micrographs will normalize micrographs to 0-mean, unit variance prior to training. For datasets with little junk, this parameter can possibly worsen training as it makes it more difficult to differentiate particles. However, it reduces the impact of junk which can improve performance in datasets with prevalent junk.