See our collection for all versions of Depth Anything V1.

Run Depth Anything V1 with Keras 3: JAX, PyTorch, or TensorFlow

kerasformers/depth_anything_base

Paper: Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data (arXiv:2401.10891) · HF Papers

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.

Pure-Keras 3 conversion of LiheYoung/depth-anything-base-hf for kerasformers. One implementation runs unmodified on TensorFlow / Torch / JAX.

This is a monocular depth checkpoint (DepthAnythingV1DepthEstimation) with relative inverse depth.

✨ Quick start

import os
os.environ["KERAS_BACKEND"] = "torch"  # or "jax" / "tensorflow"

from PIL import Image
from kerasformers.models.depth_anything_v1 import DepthAnythingV1DepthEstimation, DepthAnythingV1ImageProcessor

model = DepthAnythingV1DepthEstimation.from_weights("kerasformers/depth_anything_base")
processor = DepthAnythingV1ImageProcessor.from_weights("kerasformers/depth_anything_base")

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("kerasformers/<variant>"):

Variant	Hub	Backbone
`depth_anything_small`	`kerasformers/depth_anything_small`	ViT-S/14
`depth_anything_base`	`kerasformers/depth_anything_base`	ViT-B/14
`depth_anything_large`	`kerasformers/depth_anything_large`	ViT-L/14

Tips

Set KERAS_BACKEND before importing Keras / kerasformers.
For metric (metre) depth, use Depth Anything V2 metric heads.
See Depth Anything V1 docs and Loading Weights.
Community / upstream weights: DepthAnythingV1DepthEstimation.from_weights("hf:LiheYoung/depth-anything-base-hf").