Depth Estimation
Transformers
Safetensors
PyTorch
English
depth_anything
computer-vision
absolute depth
Instructions to use Boxiang/depth_chm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Boxiang/depth_chm with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("depth-estimation", model="Boxiang/depth_chm")# Load model directly from transformers import AutoImageProcessor, AutoModelForDepthEstimation processor = AutoImageProcessor.from_pretrained("Boxiang/depth_chm") model = AutoModelForDepthEstimation.from_pretrained("Boxiang/depth_chm") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8f353af4cfc1f9af6d8491c74a7a90a886ff0108d766eff60d981169d976ad2c
- Size of remote file:
- 390 MB
- SHA256:
- cd8b2a891e69cfe9c3248ee172a37b918d7533e8ca67fbf953c584aceedbbd6d
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