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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.

ClsHead

class mmcls.models.ClsHead(loss={'loss_weight': 1.0, 'type': 'CrossEntropyLoss'}, topk=(1,), cal_acc=False, init_cfg=None)[source]

classification head.

Parameters
  • loss (dict) – Config of classification loss.

  • topk (int | tuple) – Top-k accuracy.

  • cal_acc (bool) – Whether to calculate accuracy during training. If you use Mixup/CutMix or something like that during training, it is not reasonable to calculate accuracy. Defaults to False.

simple_test(cls_score, softmax=True, post_process=True)[source]

Inference without augmentation.

Parameters
  • cls_score (tuple[Tensor]) – The input classification score logits. Multi-stage inputs are acceptable but only the last stage will be used to classify. The shape of every item should be (num_samples, num_classes).

  • softmax (bool) – Whether to softmax the classification score.

  • post_process (bool) – Whether to do post processing the inference results. It will convert the output to a list.

Returns

The inference results.

  • If no post processing, the output is a tensor with shape (num_samples, num_classes).

  • If post processing, the output is a multi-dimentional list of float and the dimensions are (num_samples, num_classes).

Return type

Tensor | list

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