Create README.md (#1)

- Create README.md (504d9b1853f3ebfb40f16f2cf1e7178482969c47)

README.md

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+---
+license: mit
+---
+
+# 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.
+
+![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.
+
+## How to use
+
+Here is how to use this model for zero-shot depth estimation on an image:
+
+```python
+from transformers import DPTImageProcessor, DPTForDepthEstimation
+import torch
+import numpy as np
+from PIL import Image
+import requests
+
+url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+image = Image.open(requests.get(url, stream=True).raw)
+
+processor = DPTImageProcessor.from_pretrained("Intel/dpt-large")
+model = DPTForDepthEstimation.from_pretrained("Intel/dpt-large")
+
+# prepare image for the model
+inputs = processor(images=image, return_tensors="pt")
+
+with torch.no_grad():
+    outputs = model(**inputs)
+    predicted_depth = outputs.predicted_depth
+
+# interpolate to original size
+prediction = torch.nn.functional.interpolate(
+    predicted_depth.unsqueeze(1),
+    size=image.size[::-1],
+    mode="bicubic",
+    align_corners=False,
+)
+
+# visualize the prediction
+output = prediction.squeeze().cpu().numpy()
+formatted = (output * 255 / np.max(output)).astype("uint8")
+depth = Image.fromarray(formatted)
+```
+
+or one can use the pipeline API:
+
+```python
+from transformers import pipeline
+
+pipe = pipeline(task="depth-estimation", model="Intel/dpt-beit-base-384")
+result = pipe("http://images.cocodataset.org/val2017/000000039769.jpg")
+result["depth"]
+```