What would be the state of the art image captioning deep learning model? I saw a couple of architectures, like CNN-LSTM, with and without attention model, use of Glove vector, self-critical models, etc. I am overwhelmed looking at different notebooks and architectures, came here for a guidance.  I am looking to build a personal project on image annotations. Also, if I wanted to use this deep learning model together with TFX pipeline, what would be the best type of architecture I can go with?
Here are a couple of Kaggle Kernels, Notebooks and Tutorials for Image Captioning.

Kaggle Kernel | Neural Image Captioning:  -&gt; 

Kaggle Kernel | Show Attend and Tell

Kaggle Kernel | Flickr Image Captioning : TPU, TF2 &amp; Glove

Tensorflow Tutorial | Image captioning with visual attention

Show and Tell: A Neural Image Caption Generator by Vinyals et al.