Depth Estimation
Transformers
Safetensors
video_depth_anything
feature-extraction
video-depth-anything
video
custom_code
Instructions to use bigshanedogg/Metric-Video-Depth-Anything-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bigshanedogg/Metric-Video-Depth-Anything-Base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("depth-estimation", model="bigshanedogg/Metric-Video-Depth-Anything-Base", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("bigshanedogg/Metric-Video-Depth-Anything-Base", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 694 Bytes
f258387 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | """Video Depth Anything processor: ``VideoDepthAnythingImageProcessor`` (the official per-frame transform) as its only part."""
from transformers.processing_utils import ProcessorMixin
class VideoDepthAnythingProcessor(ProcessorMixin):
attributes = ["image_processor"]
def __init__(self, image_processor=None, **kwargs):
super().__init__(image_processor, **kwargs)
def __call__(self, videos=None, **kwargs):
return self.image_processor(videos, **kwargs)
def post_process_depth_estimation(self, outputs, target_sizes=None):
return self.image_processor.post_process_depth_estimation(outputs, target_sizes)
__all__ = ["VideoDepthAnythingProcessor"]
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