depth-anything
/

Depth-Anything-V2-Metric-Indoor-Small-hf

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 ---
 library_name: transformers
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 ---
 library_name: transformers
+tags:
+- depth
+- absolute depth
+pipeline_tag: depth-estimation
 ---
+# Depth Anything V2 (Fine-tuned for Metric Depth Estimation) - Transformers Version
+This model represents a fine-tuned version of [Depth Anything V2](https://huggingface.co/depth-anything/Depth-Anything-V2-Large-hf) for indoor metric depth estimation using the synthetic Hypersim datasets.
+The model checkpoint is compatible with the transformers library.
+Depth Anything V2 was introduced in [the paper of the same name](https://arxiv.org/abs/2406.09414) by Lihe Yang et al. It uses the same architecture as the original Depth Anything release but employs synthetic data and a larger capacity teacher model to achieve much finer and robust depth predictions. This fine-tuned version for metric depth estimation was first released in [this repository](https://github.com/DepthAnything/Depth-Anything-V2).
+## Model description
+Depth Anything V2 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 ~600K synthetic labeled images and ~62 million real unlabeled 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="depth-anything/Depth-Anything-V2-Metric-Indoor-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, you can use the model and processor classes:
+```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("depth-anything/Depth-Anything-V2-Metric-Indoor-Small-hf")
+model = AutoModelForDepthEstimation.from_pretrained("depth-anything/Depth-Anything-V2-Metric-Indoor-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, please refer to the [documentation](https://huggingface.co/transformers/main/model_doc/depth_anything.html#).
+## Citation
+```bibtex
+@article{depth_anything_v2,
+  title={Depth Anything V2},
+  author={Yang, Lihe and Kang, Bingyi and Huang, Zilong and Zhao, Zhen and Xu, Xiaogang and Feng, Jiashi and Zhao, Hengshuang},
+  journal={arXiv:2406.09414},
+  year={2024}
+}
+@inproceedings{depth_anything_v1,
+  title={Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data},
+  author={Yang, Lihe and Kang, Bingyi and Huang, Zilong and Xu, Xiaogang and Feng, Jiashi and Zhao, Hengshuang},
+  booktitle={CVPR},
+  year={2024}
+}
+```