Depth Estimation
Transformers
Safetensors
prompt_depth_anything
prompt-depth-anything
metric-depth
custom_code
Instructions to use bigshanedogg/Prompt-Depth-Anything-Small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bigshanedogg/Prompt-Depth-Anything-Small with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("depth-estimation", model="bigshanedogg/Prompt-Depth-Anything-Small", 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", trust_remote_code=True) model = AutoModelForDepthEstimation.from_pretrained("bigshanedogg/Prompt-Depth-Anything-Small", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 492 Bytes
29ebc1d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | {
"auto_map": {
"AutoProcessor": "processing_prompt_depth_anything.PromptDepthAnythingProcessor"
},
"image_processor": {
"auto_map": {
"AutoImageProcessor": "image_processing_prompt_depth_anything.PromptDepthAnythingImageProcessor",
"AutoProcessor": "processing_prompt_depth_anything.PromptDepthAnythingProcessor"
},
"image_processor_type": "PromptDepthAnythingImageProcessor",
"max_size": 1008
},
"processor_class": "PromptDepthAnythingProcessor"
}
|