Note

You are reading the documentation for MMClassification 0.x, which will soon be deprecated at the end of 2022. We recommend you upgrade to MMClassification 1.0 to enjoy fruitful new features and better performance brought by OpenMMLab 2.0. Check the installation tutorial, migration tutorial and changelog for more details.

mmcls.core

This package includes some runtime components. These components are useful in classification tasks but not supported by MMCV yet.

Note

Some components may be moved to MMCV in the future.

Evaluation

Evaluation metrics calculation functions

precision	Calculate precision according to the prediction and target.
recall	Calculate recall according to the prediction and target.
f1_score	Calculate F1 score according to the prediction and target.
precision_recall_f1	Calculate precision, recall and f1 score according to the prediction and target.
average_precision	Calculate the average precision for a single class.
mAP	Calculate the mean average precision with respect of classes.
support	Calculate the total number of occurrences of each label according to the prediction and target.
average_performance	Calculate CP, CR, CF1, OP, OR, OF1, where C stands for per-class average, O stands for overall average, P stands for precision, R stands for recall and F1 stands for F1-score.
calculate_confusion_matrix	Calculate confusion matrix according to the prediction and target.

Hook

ClassNumCheckHook
PreciseBNHook	Precise BN hook.
CosineAnnealingCooldownLrUpdaterHook	Cosine annealing learning rate scheduler with cooldown.
MMClsWandbHook	Enhanced Wandb logger hook for classification.

Optimizers

Lamb	A pure pytorch variant of FuseLAMB (NvLamb variant) optimizer.