Best Model for Multi-label image classification Usually using CNN architecture with a Sigmoid function as an activation function in the last layer and using binary cross entropy can be used to output a probability for each class. However, the problem is when we have more than 2 labels per image, the model will result in lower accuracy. Anyone has a suggestion for a robust state-of-art models that do Multi-label image classification  with a high accuracy.
The models will stay the same for the case like ResNets, EfficientNets, NFNets and MobileNet amongst many others. To start with go EffNetB4, good training speed and accuracy. These are great feature extractors what you do with them bring another complexity so in multi-label, look at using label_smoothing for regularization.