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README.md

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+---
+language: en
+license: mit
+tags:
+- vision
+- image-to-text
+model_name: microsoft/git-large-textvqa
+---
+
+# GIT (GenerativeImage2Text), large-sized, fine-tuned on TextVQA
+
+GIT (short for GenerativeImage2Text) model, large-sized version, fine-tuned on TextVQA. It was introduced in the paper [GIT: A Generative Image-to-text Transformer for Vision and Language](https://arxiv.org/abs/2205.14100) by Wang et al. and first released in [this repository](https://github.com/microsoft/GenerativeImage2Text).
+
+Disclaimer: The team releasing GIT did not write a model card for this model so this model card has been written by the Hugging Face team.
+
+## Model description
+
+GIT is a Transformer decoder conditioned on both CLIP image tokens and text tokens. The model is trained using "teacher forcing" on a lot of (image, text) pairs.
+
+The goal for the model is simply to predict the next text token, giving the image tokens and previous text tokens.
+
+The model has full access to (i.e. a bidirectional attention mask is used for) the image patch tokens, but only has access to the previous text tokens (i.e. a causal attention mask is used for the text tokens) when predicting the next text token.
+
+![GIT architecture](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/git_architecture.jpg)
+
+This allows the model to be used for tasks like:
+
+- image and video captioning
+- visual question answering (VQA) on images and videos
+- even image classification (by simply conditioning the model on the image and asking it to generate a class for it in text).
+
+## Intended uses & limitations
+
+You can use the raw model for visual question answering (VQA). See the [model hub](https://huggingface.co/models?search=microsoft/git) to look for
+fine-tuned versions on a task that interests you.
+
+### How to use
+
+For code examples, we refer to the [documentation](https://huggingface.co/transformers/main/model_doc/git.html).
+
+## 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 TextVQA.
+
+See table 11 in the [paper](https://arxiv.org/abs/2205.14100) for more details.
+
+### Preprocessing
+
+We refer to the original repo regarding details for preprocessing during training.
+
+During validation, one resizes the shorter edge of each image, after which center cropping is performed to a fixed-size resolution. Next, frames are normalized across the RGB channels with the ImageNet mean and standard deviation.
+
+## Evaluation results
+
+For evaluation results, we refer readers to the [paper](https://arxiv.org/abs/2205.14100).