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pipeline_tag: depth-estimation
license: apache-2.0
base_model: LiheYoung/depth-anything-small-hf
library_name: zeromodels
tags:
- keras
- zeromodels
- depth-anything
- depth-estimation
- arxiv:2401.10891
- pytorch
- jax
- tf
---
## ***See [our collection](https://huggingface.co/collections/zeromodels/depth-anything-v1-and-v2-6a8eaf5352197613b1655ac5) for all versions of Depth Anything V1.***
# Run Depth Anything V1 with Keras 3: JAX, PyTorch, or TensorFlow
[](https://github.com/IMvision12/ZeroModels) [](https://imvision12.github.io/ZeroModels/depth_anything_v1/) [](https://huggingface.co/collections/zeromodels/depth-anything-v1-and-v2-6a8eaf5352197613b1655ac5)
# zeromodels/depth_anything_small
Paper: [Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data (arXiv:2401.10891)](https://arxiv.org/abs/2401.10891) · [HF Papers](https://huggingface.co/papers/2401.10891)
Depth Anything estimates depth from a single image. A DINOv2 ViT backbone feeds a DPT-style neck and head. V1 outputs relative inverse depth (larger means closer; units are arbitrary within one image).
For more details on the model, please go to the upstream [model card](https://huggingface.co/LiheYoung/depth-anything-small-hf).
Pure-**Keras 3** conversion of [`LiheYoung/depth-anything-small-hf`](https://huggingface.co/LiheYoung/depth-anything-small-hf) for [zeromodels](https://github.com/IMvision12/ZeroModels). One implementation runs unmodified on **TensorFlow / Torch / JAX**.
This is a **monocular depth** checkpoint (`DepthAnythingV1DepthEstimation`) with relative inverse depth.
## ✨ Quick start
```python
import os
os.environ["KERAS_BACKEND"] = "torch" # or "jax" / "tensorflow"
from PIL import Image
from zeromodels.models.depth_anything_v1 import DepthAnythingV1DepthEstimation, DepthAnythingV1ImageProcessor
model = DepthAnythingV1DepthEstimation.from_weights("zeromodels/depth_anything_small")
processor = DepthAnythingV1ImageProcessor.from_weights("zeromodels/depth_anything_small")
image = Image.open("your_image.jpg").convert("RGB")
output = model(processor(image)["pixel_values"], training=False)
depth = processor.post_process_depth_estimation(
output, original_size=(image.height, image.width)
)
print(depth.shape)
```
Load any Depth Anything V1 variant the same way with `from_weights("zeromodels/<variant>")`:
| Variant | Hub | Backbone |
|---|---|---|
| `depth_anything_small` | [`zeromodels/depth_anything_small`](https://huggingface.co/zeromodels/depth_anything_small) | ViT-S/14 |
| `depth_anything_base` | [`zeromodels/depth_anything_base`](https://huggingface.co/zeromodels/depth_anything_base) | ViT-B/14 |
| `depth_anything_large` | [`zeromodels/depth_anything_large`](https://huggingface.co/zeromodels/depth_anything_large) | ViT-L/14 |
## Tips
- Set `KERAS_BACKEND` **before** importing Keras / zeromodels.
- For metric (metre) depth, use Depth Anything V2 metric heads.
- See [Depth Anything V1 docs]({DOCS_URL}) and [Loading Weights](https://imvision12.github.io/ZeroModels/loading_weights/).
- Community / upstream weights: `DepthAnythingV1DepthEstimation.from_weights("hf:LiheYoung/depth-anything-small-hf")`.
## Special Thanks
A huge thank you to the Depth Anything authors for creating and releasing these models.
License: Apache 2.0.
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