Depth Estimation
Transformers
Safetensors
DepthAnythingV2
depth_anything_v2
feature-extraction
custom_code
Instructions to use bigshanedogg/Depth-Anything-V2-Small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bigshanedogg/Depth-Anything-V2-Small with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("depth-estimation", model="bigshanedogg/Depth-Anything-V2-Small", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("bigshanedogg/Depth-Anything-V2-Small", trust_remote_code=True, device_map="auto") - DepthAnythingV2
How to use bigshanedogg/Depth-Anything-V2-Small 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-Small", 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
File size: 685 Bytes
397f342 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | """Depth Anything V2 processor: ``DepthAnythingV2ImageProcessor`` (the official image2tensor transform) as its only part."""
from transformers.processing_utils import ProcessorMixin
class DepthAnythingV2Processor(ProcessorMixin):
attributes = ["image_processor"]
def __init__(self, image_processor=None, **kwargs):
super().__init__(image_processor, **kwargs)
def __call__(self, images=None, **kwargs):
return self.image_processor(images, **kwargs)
def post_process_depth_estimation(self, outputs, target_sizes=None):
return self.image_processor.post_process_depth_estimation(outputs, target_sizes)
__all__ = ["DepthAnythingV2Processor"]
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