Comments (2)
Hi!
The reported f1 score during training is the classification f1 score, not the actual localization f1 score. We also found that the model with best validation loss typically performs better then the model with the best validation f1 score.
Regarding the sphereface loss, here is one config we used a while ago:
{
"identifier": "SiameseNet",
"network_config": {
"output_channels": 128,
"dropout": 0.157062,
"repeat_layers": 0,
"norm_name": "GroupNorm",
"norm_kwargs": {
"num_groups": 32
}
},
"train_config":{
"loss": "SphereFaceLoss",
"tl_margin": null,
"sf_margin": 6.0,
"sf_scale": 1.51195,
"miner": false,
"miner_margin": null,
"learning_rate": 7.914e-05,
"batchsize": 51,
"num_classes": 27
}
}
In general, this function might help you to create others:
https://github.com/MPI-Dortmund/tomotwin-cryoet/blob/main/tomotwin/train_main.py#L570C5-L570C18
To be honest, this part of TomoTwin could be much more flexible.
from tomotwin-cryoet.
May I ask how the num_classes is set for this? Doesn't it represent all the classes?
Best regards,
Chi
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