Depth Estimation
Transformers
Safetensors
camera_depth_model
feature-extraction
camera-depth-model
rgbd
robotics
custom_code
Instructions to use bigshanedogg/Camera-Depth-Model-D405 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bigshanedogg/Camera-Depth-Model-D405 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("depth-estimation", model="bigshanedogg/Camera-Depth-Model-D405", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("bigshanedogg/Camera-Depth-Model-D405", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 997 Bytes
61d81fd | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 | """Camera Depth Model configuration: the constructor arguments of the official ``RGBDDepth``
(ByteDance-Seed/manip-as-in-sim-suite @ 757b2ea2, cdm/rgbddepth/dpt.py) and the preprocessing defaults of cdm/infer.py."""
from transformers import PreTrainedConfig
class CameraDepthModelConfig(PreTrainedConfig):
r"""
encoder: "vits" | "vitb" | "vitl" | "vitg" (infer.py model_configs; the released models are vitl).
features / out_channels / use_bn / use_clstoken: the DPT head arguments (infer.py model_configs).
max_depth: the ``RGBDDepth`` argument (stored; the forward does not use it).
camera: the depth camera the checkpoint is trained for (None for the base model).
"""
model_type = "camera_depth_model"
encoder: str = "vitl"
features: int = 256
out_channels: list | tuple = (256, 512, 1024, 1024)
use_bn: bool = False
use_clstoken: bool = False
max_depth: float = 20.0
camera: str | None = None
__all__ = ["CameraDepthModelConfig"]
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