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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.

MultiLabelClsHead

class mmcls.models.MultiLabelClsHead(loss={'loss_weight': 1.0, 'reduction': 'mean', 'type': 'CrossEntropyLoss', 'use_sigmoid': True}, init_cfg=None)[source]

Classification head for multilabel task.

Parameters

loss (dict) – Config of classification loss.

simple_test(x, sigmoid=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).

  • sigmoid (bool) – Whether to sigmoid 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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