Create README.md

README.md

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+---
+license: apache-2.0
+tags:
+- vision
+- depth-estimation
+widget:
+- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
+  example_title: Tiger
+- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
+  example_title: Teapot
+- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
+  example_title: Palace
+---
+
+# DPT-Hybrid 
+
+Dense Prediction Transformer (DPT) 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. and first released in [this repository](https://github.com/isl-org/DPT). This repository hosts the "hybrid" version of the model as stated in the paper.
+
+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
+
+DPT uses the Vision Transformer (ViT) 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)
+
+DPT-Hybrid diverges from DPT by using [ViT-hybrid](https://huggingface.co/google/vit-hybrid-base-bit-384) as a backbone and taking some activations from the backbone.
+
+## Intended uses & limitations
+
+You can use the raw model for zero-shot monocular depth estimation. See the [model hub](https://huggingface.co/models?search=dpt) to look for
+fine-tuned versions on a task that interests you.
+
+### How to use
+
+Here is how to use this model for zero-shot depth estimation on an image:
+
+```python
+from PIL import Image
+import numpy as np
+import requests
+import torch
+
+
+from transformers import DPTForDepthEstimation, DPTFeatureExtractor
+
+model = DPTForDepthEstimation.from_pretrained("Intel/dpt-hybrid-midas", low_cpu_mem_usage=True)
+feature_extractor = DPTFeatureExtractor.from_pretrained("Intel/dpt-hybrid-midas")
+
+url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+image = Image.open(requests.get(url, stream=True).raw)
+
+# prepare image for the model
+inputs = feature_extractor(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)
+depth.show()
+
+For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/dpt).
+
+### BibTeX entry and citation info
+
+```bibtex
+@article{DBLP:journals/corr/abs-2103-13413,
+  author    = {Ren{\'{e}} Ranftl and
+               Alexey Bochkovskiy and
+               Vladlen Koltun},
+  title     = {Vision Transformers for Dense Prediction},
+  journal   = {CoRR},
+  volume    = {abs/2103.13413},
+  year      = {2021},
+  url       = {https://arxiv.org/abs/2103.13413},
+  eprinttype = {arXiv},
+  eprint    = {2103.13413},
+  timestamp = {Wed, 07 Apr 2021 15:31:46 +0200},
+  biburl    = {https://dblp.org/rec/journals/corr/abs-2103-13413.bib},
+  bibsource = {dblp computer science bibliography, https://dblp.org}
+}
+```