Deep Picker Trainjob. Both of these jobs require the same inputs and produce the same outputs as listed below:
Deep Picker Trainjob features various parameters. The training parameters are detailed below:
Deep Picker Trainjob is complete, it will output two plots indicating the performance on the training and validation sets over each epoch. The first plot presents the training and validation losses, while the second plot presents the training and validation accuracies. The x-axis for both plots indicates the epoch and the y-axis indicates the data that the corresponding plot is presenting. Successfully trained models will have losses that decrease overtime and accuracies that increase overtime. It should be noted that losses and accuracies may change in an undesirable fashion and then correct itself with more training.
Deep Picker Trainjob fails during training, the job will still output a Deep Picker Model. This model will be the model at the epoch with the lowest validation loss prior to failure. The training on this model can be resumed by passing this output as the Deep Picker Model input of another
Deep Picker Trainjob then setting the "resume training" parameter on. The job will then continue training from the point that it saved. Otherwise, the job will begin training anew using the input model parameters as an initialization.
Deep Picker Trainjob has been used to train a deep particle picking model it can be used to pick particles from micrographs using the
Deep Picker Inferencejob. This job has the following inputs and outputs:
Deep Picker Inferencejob features various parameters. The parameters are detailed below:
Deep Picker Inference
Deep Picker Inferencejob can be observed and have a threshold applied using the Inspect Particle Picks job. This job interacts with particle picks from
Deep Picker Inferencedifferently in that it enable a user to apply a threshold based on Topaz model performance rather than power score. To do so, vary the power score threshold in the
Inspect Particle Picksjob. This number is the a percentage indicating how confident the model is with its prediction.
Extract from Micrographsjob in cryoSPARC. This updates the CTF information within the particle picks and makes the picks compatible with other cryoSPARC jobs such as