 Training data  From the paper:  > We collect 0.8B image-text pairs for pre-training, which include COCO (Lin et al., 2014), Conceptual Captions (CC3M) (Sharma et al., 2018), SBU (Ordonez et al., 2011), Visual Genome (VG) (Krishna et al., 2016), Conceptual Captions (CC12M) (Changpinyo et al., 2021), ALT200M (Hu et al., 2021a), and an extra 0.6B data following a similar collection procedure in Hu et al. (2021a).  => however this is for the model referred to as "GIT" in the paper, which is not open-sourced.  This checkpoint is "GIT-large", which is a smaller variant of GIT trained on 20 million image-text pairs.  Next, the model was fine-tuned on TextCaps.  See table 11 in the [paper](https://arxiv.org/abs/2205.14100) for more details.  