- Libraries
- ZeroModels
How to use zeromodels/depth_anything_v2_large with ZeroModels:
# pip install -U zeromodels
# ZeroModels is pure Keras 3, so pick a backend: "jax", "torch" or "tensorflow".
import os
os.environ["KERAS_BACKEND"] = "jax"
from zeromodels import AutoZModel
# AutoZModel reads the repo's model_type and loads the matching class.
# For a task head use the matching loader, e.g. AutoZMImageClassify / AutoZMDetect /
# AutoZMSemanticSegment / AutoZMTextGenerate (see zeromodels.auto).
model = AutoZModel.from_weights("zeromodels/depth_anything_v2_large")
- Keras
How to use zeromodels/depth_anything_v2_large with Keras:
# !pip install -U keras tensorflow huggingface_hub
# Keras needs TensorFlow installed to read "hf://" paths, so the tensorflow backend is selected here;
# "jax" and "torch" also work for computation once TensorFlow is installed.
import os
os.environ["KERAS_BACKEND"] = "tensorflow"
import keras
model = keras.saving.load_model("hf://zeromodels/depth_anything_v2_large")
- DepthAnythingV2
How to use zeromodels/depth_anything_v2_large 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="zeromodels/depth_anything_v2_large", 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
