In its current form the step predicts image captions using ClipClap. ClipClap first embeds images using the Clip model, which has been pre-trained on 400M image/text pairs to pick out an image’s correct caption from a list of candidates. These images are then projected into the embedding space of the GPT-2 language model, using a custom model trained for the task. Finally, using this projection as a prefix, the pretained GPT-2 is asked to predict the next sentence, i.e. the one following the image.

model
string
default: "TRF"

Which projection model to use. The projection model maps embeddings from the pretrained Clip image model, to the pretrained GPT-2 language model. Select between a multi-layer perceptron (“MLP”), or the faster transformer (“TRF”).

Values must be one of the following:

  • TRF
  • MLP
weights
string

Select the parameter set for the model. The ClipClap authors provide weights for models having been trained either on the COCO dataset (“coco”) or the ConceptualCaptions dataset (“concept”).

Values must be one of the following:

  • coco
  • concept
beam_search
boolean

Whether to use beam-search or greedy word prediction. When enabled, uses a more expensive but “smarter” algorithm to predict the words in the captions.

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