Update README.md

README.md

@@ -1,3 +1,98 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+tags:
+- vision
+pipeline_tag: depth-estimation
+widget:
+- inference: false
+---
+
+# Depth Anything (small-sized model, Transformers version) 
+
+Depth Anything model. It was introduced in the paper [Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data](https://arxiv.org/abs/2401.10891) by Lihe Yang et al. and first released in [this repository](https://github.com/LiheYoung/Depth-Anything).
+
+[Online demo](https://huggingface.co/spaces/LiheYoung/Depth-Anything) is also provided.
+
+Disclaimer: The team releasing Depth Anything did not write a model card for this model so this model card has been written by the Hugging Face team.
+
+## Model description
+
+Depth Anything leverages the [DPT](https://huggingface.co/docs/transformers/model_doc/dpt) architecture with a [DINOv2](https://huggingface.co/docs/transformers/model_doc/dinov2) backbone.
+
+The model is trained on ~62 million images, obtaining state-of-the-art results for both relative and absolute depth estimation.
+
+<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/depth_anything_overview.jpg"
+alt="drawing" width="600"/>
+
+<small> Depth Anything overview. Taken from the <a href="https://arxiv.org/abs/2401.10891">original paper</a>.</small>
+
+## Intended uses & limitations
+
+You can use the raw model for tasks like zero-shot depth estimation. See the [model hub](https://huggingface.co/models?search=depth-anything) to look for
+other versions on a task that interests you.
+
+### How to use
+
+Here is how to use this model to perform zero-shot depth estimation:
+
+```python
+from transformers import pipeline
+from PIL import Image
+import requests
+
+# load pipe
+pipe = pipeline(task="depth-estimation", model="LiheYoung/depth-anything-small-hf")
+
+# load image
+url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
+image = Image.open(requests.get(url, stream=True).raw)
+
+# inference
+depth = pipe(image)["depth"]
+```
+
+Alternatively, one can use the classes themselves:
+
+```python
+from transformers import AutoImageProcessor, AutoModelForDepthEstimation
+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)
+
+image_processor = AutoImageProcessor.from_pretrained("LiheYoung/depth-anything-small-hf")
+model = AutoModelForDepthEstimation.from_pretrained("LiheYoung/depth-anything-small-hf")
+
+# prepare image for the model
+inputs = image_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,
+)
+```
+For more code examples, we refer to the [documentation](https://huggingface.co/transformers/main/model_doc/depth_anything.html#).
+
+
+### BibTeX entry and citation info
+
+```bibtex
+@misc{yang2024depth,
+      title={Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data}, 
+      author={Lihe Yang and Bingyi Kang and Zilong Huang and Xiaogang Xu and Jiashi Feng and Hengshuang Zhao},
+      year={2024},
+      eprint={2401.10891},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV}
+}
+```