depth-anything
/

Depth-Anything-V2-Large-hf

@@ -1,199 +1,108 @@
 ---
 library_name: transformers
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 ---
 library_name: transformers
+library: transformers
+license: cc-by-nc-4.0
+tags:
+- depth
+- relative depth
+pipeline_tag: depth-estimation
+widget:
+- inference: false
 ---
+# Depth Anything V2 Base – Transformers Version
+Depth Anything V2 is trained from 595K synthetic labeled images and 62M+ real unlabeled images, providing the most capable monocular depth estimation (MDE) model with the following features:
+- more fine-grained details than Depth Anything V1
+- more robust than Depth Anything V1 and SD-based models (e.g., Marigold, Geowizard)
+- more efficient (10x faster) and more lightweight than SD-based models
+- impressive fine-tuned performance with our pre-trained models
+This 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 uses synthetic data and a larger capacity teacher model to achieve much finer and robust depth predictions. The original Depth Anything model 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 was first released in [this repository](https://github.com/LiheYoung/Depth-Anything).
+[Online demo](https://huggingface.co/spaces/depth-anything/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-Large-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-Large-hf")
+model = AutoModelForDepthEstimation.from_pretrained("depth-anything/Depth-Anything-V2-Large-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
+@misc{yang2024depth,
+      title={Depth Anything V2},
+      author={Lihe Yang and Bingyi Kang and Zilong Huang and Zhen Zhao and Xiaogang Xu and Jiashi Feng and Hengshuang Zhao},
+      year={2024},
+      eprint={2406.09414},
+      archivePrefix={arXiv},
+      primaryClass={id='cs.CV' full_name='Computer Vision and Pattern Recognition' is_active=True alt_name=None in_archive='cs' is_general=False description='Covers image processing, computer vision, pattern recognition, and scene understanding. Roughly includes material in ACM Subject Classes I.2.10, I.4, and I.5.'}
+}
+```