- Libraries
- KerasFormers
How to use kerasformers/depth_anything_v2_large with KerasFormers:
# 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
- Keras
How to use kerasformers/depth_anything_v2_large with Keras:
# Available backend options are: "jax", "torch", "tensorflow".
import os
os.environ["KERAS_BACKEND"] = "jax"
import keras
model = keras.saving.load_model("hf://kerasformers/depth_anything_v2_large")
- DepthAnythingV2
How to use kerasformers/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 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="kerasformers/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).eval()
raw_img = cv2.imread("your/image/path")
depth = model.infer_image(raw_img) # HxW raw depth map in numpy
- Notebooks
- Google Colab
- Kaggle
