| --- |
| license: apache-2.0 |
| library_name: transformers |
| base_model: depth-anything/DA3-BASE |
| tags: |
| - depth-anything-3 |
| - depth-estimation |
| - multi-view-geometry |
| pipeline_tag: image-to-3d |
| --- |
| |
| # DA3-BASE — transformers format |
|
|
| Unofficial transformers port of **Depth Anything 3** `DA3-BASE` (any-view model; relative depth, camera pose, pose conditioning). Loads with |
| `trust_remote_code`; the `depth_anything_3` package is not needed. `model.safetensors` holds the tensors of |
| `depth-anything/DA3-BASE`, unchanged (re-saved by `save_pretrained`; every tensor checked equal). |
|
|
| > **Not an official release.** Depth Anything 3 was created by Haotong Lin, Sili Chen, Jun Hao Liew, Donny Y. Chen, |
| > Zhenyu Li, Guang Shi, Jiashi Feng and Bingyi Kang (ByteDance Seed) — https://github.com/ByteDance-Seed/depth-anything-3, |
| > paper *"Depth Anything 3: Recovering the Visual Space from Any Views"* (arXiv 2511.10647). |
|
|
| ## What it is |
|
|
| `DepthAnything3ForDepthEstimation.model` is the package's network, built from the checkpoint's own object graph (`config.json` → |
| `da3_config`, identical to the package's `configs/da3-base.yaml`) the way `depth_anything_3.cfg.create_object` |
| builds it; `forward` runs the network's `forward` and returns its outputs as a `DepthAnything3Output` (`ModelOutput`; |
| the network's `depth` is named `predicted_depth`, the other keys keep their names). The network code is the official code |
| (ByteDance-Seed/depth-anything-3 @ 3d835ec1) vendored into flat files (`da3_*.py`); the only edits are imports and one |
| renamed logger. `DepthAnything3Processor` (`processing_depth_anything_3.py`, `processor_config.json`) wraps |
| `DepthAnything3ImageProcessor`, which runs the package's `InputProcessor`. |
|
|
| Verified against the official package (same process and seeds, V100, 4 images): preprocessing and every output of the |
| plain, pose-conditioned and ray-pose forward are **bit-identical** (also as a tuple), in fp32 and under the package API's autocast. |
|
|
| ## Usage |
|
|
| ```python |
| import torch |
| from PIL import Image |
| from transformers import AutoModelForDepthEstimation, AutoProcessor |
| |
| model = AutoModelForDepthEstimation.from_pretrained("DA3-BASE", trust_remote_code=True).eval().cuda() |
| processor = AutoProcessor.from_pretrained("DA3-BASE", trust_remote_code=True) |
| inputs = processor(images=[Image.open(p) for p in paths], return_tensors="pt").to("cuda") # pixel_values (1, N, 3, H, W) |
| dtype = torch.bfloat16 if torch.cuda.is_bf16_supported() else torch.float16 # the package API's autocast |
| with torch.no_grad(), torch.autocast("cuda", dtype=dtype): |
| outputs = model(**inputs) # outputs.predicted_depth (1, N, H, W), outputs.depth_conf, ... |
| ``` |
|
|
| - `AutoModel` loads the same class; `return_dict=False` returns a tuple. Depth is at the processed resolution, as the |
| package API returns it. |
| - Backbone features: `export_feat_layers=[i, ...]` returns them in `outputs.aux`. |
| - Pose conditioning: pass `extrinsics` (1, N, 4, 4) / `intrinsics` (1, N, 3, 3) — give them to the processor with the images so the intrinsics follow the resize. `use_ray_pose=True` decodes poses from the ray head. |
| - Some outputs use random sampling as in the original (sky handling samples depths): seed torch for reproducible runs. |
| - Runtime packages beyond torch / transformers: `addict`, `omegaconf`, `einops`, `opencv-python` (as the original). |
|
|
| ## Licensing |
|
|
| - Code and weights: **Apache-2.0** — [`LICENSE`](LICENSE) (ByteDance-Seed/depth-anything-3), as published on `depth-anything/DA3-BASE`. |
| - Attribution, sources and the changes made here: [`NOTICE`](NOTICE). |
|
|
| ## Citation |
|
|
| If you use this model, please cite the original work, as its authors ask: |
|
|
| ```bibtex |
| @article{depthanything3, |
| title={Depth Anything 3: Recovering the visual space from any views}, |
| author={Haotong Lin and Sili Chen and Jun Hao Liew and Donny Y. Chen and Zhenyu Li and Guang Shi and Jiashi Feng and Bingyi Kang}, |
| journal={arXiv preprint arXiv:2511.10647}, |
| year={2025} |
| } |
| ``` |
|
|
