Depth Estimation
Transformers
Safetensors
prompt_depth_anything
prompt-depth-anything
metric-depth
custom_code
Instructions to use bigshanedogg/Prompt-Depth-Anything-Small-Transparent with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bigshanedogg/Prompt-Depth-Anything-Small-Transparent with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("depth-estimation", model="bigshanedogg/Prompt-Depth-Anything-Small-Transparent", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForDepthEstimation processor = AutoImageProcessor.from_pretrained("bigshanedogg/Prompt-Depth-Anything-Small-Transparent", trust_remote_code=True) model = AutoModelForDepthEstimation.from_pretrained("bigshanedogg/Prompt-Depth-Anything-Small-Transparent", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 512 Bytes
e32477a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | {
"architectures": [
"PromptDepthAnythingForDepthEstimation"
],
"auto_map": {
"AutoConfig": "configuration_prompt_depth_anything.PromptDepthAnythingConfig",
"AutoModel": "modeling_prompt_depth_anything.PromptDepthAnythingForDepthEstimation",
"AutoModelForDepthEstimation": "modeling_prompt_depth_anything.PromptDepthAnythingForDepthEstimation"
},
"dtype": "float32",
"encoder": "vits",
"max_size": 1008,
"model_type": "prompt_depth_anything",
"transformers_version": "5.20.2"
}
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