Depth Estimation
Transformers
Safetensors
DepthAnythingV2
depth_anything_v2
feature-extraction
custom_code
Instructions to use bigshanedogg/Depth-Anything-V2-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bigshanedogg/Depth-Anything-V2-Base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("depth-estimation", model="bigshanedogg/Depth-Anything-V2-Base", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("bigshanedogg/Depth-Anything-V2-Base", trust_remote_code=True, device_map="auto") - DepthAnythingV2
How to use bigshanedogg/Depth-Anything-V2-Base with DepthAnythingV2:
# Install from https://github.com/DepthAnything/Depth-Anything-V2 # Load the model and infer depth from an image import cv2 import torch from huggingface_hub import hf_hub_download from depth_anything_v2.dpt import DepthAnythingV2 # instantiate the model model = DepthAnythingV2(encoder="<ENCODER>", features=<NUMBER_OF_FEATURES>, out_channels=<OUT_CHANNELS>) # load the weights filepath = hf_hub_download(repo_id="bigshanedogg/Depth-Anything-V2-Base", filename="depth_anything_v2_<ENCODER>.pth", repo_type="model") state_dict = torch.load(filepath, map_location="cpu") model.load_state_dict(state_dict) model.eval() raw_img = cv2.imread("your/image/path") depth = model.infer_image(raw_img) # HxW raw depth map in numpy - Notebooks
- Google Colab
- Kaggle
Download NOTICE from bigshanedogg/Depth-Anything-V2-Base: direct link, hf CLI and curl.
- Browser
- Download file 1.7 kB
-
https://huggingface.co/bigshanedogg/Depth-Anything-V2-Base/resolve/main/NOTICE
- Command line
-
hf download hf://bigshanedogg/Depth-Anything-V2-Base/NOTICE
-
curl -L -o NOTICE https://huggingface.co/bigshanedogg/Depth-Anything-V2-Base/resolve/main/NOTICE
1.7 kB
| Depth-Anything-V2-Base -- unofficial transformers-format package (trust_remote_code) | |
| Packaged by bigshanedogg, 2026. NOT an official release of the original authors. | |
| Components | |
| ---------- | |
| 1. Depth Anything V2 Base weights (depth_anything_v2_vitb.pth) | |
| Source: https://huggingface.co/depth-anything/Depth-Anything-V2-Base | |
| Copyright: Lihe Yang, Bingyi Kang, Zilong Huang, Zhen Zhao, Xiaogang Xu, Jiashi Feng, Hengshuang Zhao | |
| License: CC-BY-NC-4.0 (non-commercial) -- LICENSE | |
| 2. Depth Anything V2 code (depth_anything_v2/ and metric_depth/depth_anything_v2/, incl. its DINOv2 copy; vendored into dav2_*.py) | |
| Source: https://github.com/DepthAnything/Depth-Anything-V2 @ a561b849ebae10a6f5ef49e26c83cbbcd36c71bf | |
| Copyright: the Depth Anything V2 authors; DINOv2 layers Copyright (c) Meta Platforms, Inc. and affiliates. | |
| License: Apache-2.0 -- LICENSE.code | |
| Changes made in this package | |
| ---------------------------- | |
| - dav2_*.py: the official files concatenated into flat files; package-internal imports replaced by relative imports; NestedTensorBlock spelled out where dinov2.py imported it as Block. No computation changed. | |
| - configuration_ / modeling_ / image_processing_ / processing_depth_anything_v2.py: new transformers wrappers; the model runs the official DepthAnythingV2 methods (relative, or metric when max_depth is set); the image processor is image2tensor's transform. | |
| - model.safetensors: the .pth state dict converted by save_pretrained, names unchanged, every tensor checked equal. | |
| License files | |
| ------------- | |
| - LICENSE: Creative Commons Attribution-NonCommercial 4.0 International (weights) | |
| - LICENSE.code: Apache License 2.0, copy from DepthAnything/Depth-Anything-V2 (code) | |