title: Which loss function and metrics to use for multi-label classification with very high ratio of negatives to positives?
body: <p>I am training a multi-label classification model for detecting attributes of clothes. I am using transfer learning in Keras, retraining the last few layers of the vgg-19 model.</p>
  
  <p>The total number of attributes is 1000 and about 99% of them are 0s. Metrics like accuracy, precision, recall, etc., all fail, as the model can predict all zeroes and still achieve a very high score. Binary cross-entropy, hamming loss, etc., haven't worked in the case of loss functions.</p>
  
  <p>I am using the deep fashion dataset. </p>
  
  <p>So, which metrics and loss functions can I use to measure my model correctly?</p>