# GIT ## Overview The GIT model was proposed in [GIT: A Generative Image-to-text Transformer for Vision and Language](https://arxiv.org/abs/2205.14100) by Jianfeng Wang, Zhengyuan Yang, Xiaowei Hu, Linjie Li, Kevin Lin, Zhe Gan, Zicheng Liu, Ce Liu, Lijuan Wang. GIT is a decoder-only Transformer that leverages [CLIP](clip)'s vision encoder to condition the model on vision inputs besides text. The model obtains state-of-the-art results on image captioning and visual question answering benchmarks. The abstract from the paper is the following: *In this paper, we design and train a Generative Image-to-text Transformer, GIT, to unify vision-language tasks such as image/video captioning and question answering. While generative models provide a consistent network architecture between pre-training and fine-tuning, existing work typically contains complex structures (uni/multi-modal encoder/decoder) and depends on external modules such as object detectors/taggers and optical character recognition (OCR). In GIT, we simplify the architecture as one image encoder and one text decoder under a single language modeling task. We also scale up the pre-training data and the model size to boost the model performance. Without bells and whistles, our GIT establishes new state of the arts on 12 challenging benchmarks with a large margin. For instance, our model surpasses the human performance for the first time on TextCaps (138.2 vs. 125.5 in CIDEr). Furthermore, we present a new scheme of generation-based image classification and scene text recognition, achieving decent performance on standard benchmarks.* GIT architecture. Taken from the original paper. This model was contributed by [nielsr](https://huggingface.co/nielsr). The original code can be found [here](https://github.com/microsoft/GenerativeImage2Text). ## Usage tips - GIT is implemented in a very similar way to GPT-2, the only difference being that the model is also conditioned on `pixel_values`. ## Resources A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with GIT. - Demo notebooks regarding inference + fine-tuning GIT on custom data can be found [here](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/GIT). - See also: [Causal language modeling task guide](../tasks/language_modeling) If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we will review it. The resource should ideally demonstrate something new instead of duplicating an existing resource. ## GitVisionConfig [[autodoc]] GitVisionConfig ## GitVisionModel [[autodoc]] GitVisionModel - forward ## GitConfig [[autodoc]] GitConfig - all ## GitProcessor [[autodoc]] GitProcessor - __call__ ## GitModel [[autodoc]] GitModel - forward ## GitForCausalLM [[autodoc]] GitForCausalLM - forward