Instructions to use wardner/Depth-Anything-V3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- RKLLM
How to use wardner/Depth-Anything-V3 with RKLLM:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
Depth-Anything-V3 — [Depth]视觉深度信息v3
1. Overview
视觉深度信息v3,给定图像输出逐像素深度图与置信度(Depth Anything V3 单目深度估计)
This repository ships the RK182x deployment artifacts (RKNN graphs +
weights) together with the wdnwebui worker manifest
(DepthAnythingV3.json).
| Item | Value |
|---|---|
| Model ID | wardner/Depth-Anything-V3 |
| Display name | Depth-Anything-V3 |
| Model type | LLM |
| Compute target | RK182x |
| Host platform | RK3588 |
| Runtime | RKLLM + RKNN |
| Capabilities | swcap_vision_depth |
| Worker factory | depth_anything_v3 |
| Memory estimate | ~3000 MB |
| Init time | ~30 s |
| Cards required | 1 |
| Total weight size | 1.04 GB |
2. Files
| Path | Description | Size |
|---|---|---|
global_DepthAnythingV3.280x280.1view.1828.rknn |
RKNN graph (RK182x compute card, 1.82 TOPS generation) | 2.7 MB |
global_DepthAnythingV3.280x280.1view.weight |
Weights paired with the RKNN graph above | 552.7 MB |
head_DepthAnythingV3.280x280.1view.1828.rknn |
RKNN graph (RK182x compute card, 1.82 TOPS generation) | 1.8 MB |
head_DepthAnythingV3.280x280.1view.weight |
Weights paired with the RKNN graph above | 188.0 MB |
local_DepthAnythingV3.280x280.1view.1828.rknn |
RKNN graph (RK182x compute card, 1.82 TOPS generation) | 1.7 MB |
local_DepthAnythingV3.280x280.1view.weight |
Weights paired with the RKNN graph above | 316.6 MB |
DepthAnythingV3.json |
worker manifest — deliberately plain text | 1943 B |
DepthAnythingV3.jsonis deliberately not in Git LFS. It is the manual of this distribution unit and must stay plain text so it can be diffed, reviewed and previewed on the Hub.
3. Download
hf download wardner/Depth-Anything-V3 --local-dir ./Depth-Anything-V3
Or with git (requires Git LFS):
git clone https://huggingface.co/wardner/Depth-Anything-V3
If huggingface.co is unreachable from your network (DNS interception /
blocked access), point the client at a mirror instead:
HF_ENDPOINT=https://hf-mirror.com hf download wardner/Depth-Anything-V3 \
--local-dir ./Depth-Anything-V3
Note: with git clone the weights are Git LFS pointers until
git lfs pull completes. hf download fetches the real files directly, and
does not create a .git directory — neither is required for the model to
work; see §4.
4. Installation (wdnwebui)
Clone the repository into the model root so the folder holds both the weights and the worker manifest:
git clone https://huggingface.co/wardner/Depth-Anything-V3 /userdata/models/Depth-Anything-V3
Then open the web UI → Compute → the card appears under "local compute" (the backend rescans the model root every ~2 s, no restart needed) → click "enable".
The directory name does not have to match the model name: the SDK follows
"the folder the manifest was found in", located through the
WDNAI_DEPTH_ANYTHING_V3_DIR environment variable declared in the manifest.
5. SHA-256 verification
cd ./Depth-Anything-V3
sha256sum -c SHA256SUMS
All entries must report OK before deployment.
6. Caveats and limits
See the notes field of DepthAnythingV3.json — it records the
per-model specifics (sampling defaults, capability layout, side effects).
7. License
The upstream model is released under the Apache License 2.0. The files here are RKNN-converted derivatives, not unmodified upstream weights. See LICENSE and NOTICE.
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