Instructions to use zeromodels/depth_anything_v2_metric_outdoor_base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use zeromodels/depth_anything_v2_metric_outdoor_base with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://zeromodels/depth_anything_v2_metric_outdoor_base") - DepthAnythingV2
How to use zeromodels/depth_anything_v2_metric_outdoor_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 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="zeromodels/depth_anything_v2_metric_outdoor_base", filename="depth_anything_v2_<ENCODER>.pth", repo_type="model") state_dict = torch.load(filepath, map_location="cpu") model.load_state_dict(state_dict).eval() raw_img = cv2.imread("your/image/path") depth = model.infer_image(raw_img) # HxW raw depth map in numpy - Notebooks
- Google Colab
- Kaggle
Migrate to zeromodels (rename kf_*.json -> zm_*.json, fix refs in config + README, ensure tag + badge)
1a5468f verified | { | |
| "library_name": "zeromodels", | |
| "zeromodels_version": "1.1.3", | |
| "preprocessor_module": "zeromodels.models.depth_anything_v2", | |
| "preprocessor_class": "DepthAnythingV2ImageProcessor", | |
| "variant": "depth_anything_v2_metric_outdoor_base", | |
| "target_size": 518, | |
| "image_mean": [ | |
| 0.485, | |
| 0.456, | |
| 0.406 | |
| ], | |
| "image_std": [ | |
| 0.229, | |
| 0.224, | |
| 0.225 | |
| ], | |
| "data_format": null | |
| } |