Update README.md (#2)

- Update README.md (172c440f08454205a35ec8663aabd820bd741301)

README.md

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 # DPT 3.1 (BEiT backbone)
 
 DPT (Dense Prediction Transformer) model trained on 1.4 million images for monocular depth estimation. It was introduced in the paper [Vision Transformers for Dense Prediction](https://arxiv.org/abs/2103.13413) by Ranftl et al. (2021) and first released in [this repository](https://github.com/isl-org/DPT). 
-DPT uses the [BEiT](https://huggingface.co/docs/transformers/model_doc/beit) model as backbone and adds a neck + head on top for monocular depth estimation.
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-![model image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/dpt_architecture.jpg)
 
 Disclaimer: The team releasing DPT did not write a model card for this model so this model card has been written by the Hugging Face team.
 
 ## Model description
 
-The Table Transformer is equivalent to [DETR](https://huggingface.co/docs/transformers/model_doc/detr), a Transformer-based object detection model. Note that the authors decided to use the "normalize before" setting of DETR, which means that layernorm is applied before self- and cross-attention.
+This DPT model uses the [BEiT](https://huggingface.co/docs/transformers/model_doc/beit) model as backbone and adds a neck + head on top for monocular depth estimation.
+
+![model image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/dpt_architecture.jpg)
 
 ## How to use