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
Upload README.md with huggingface_hub
Browse files
README.md
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# Depth-CHM Model
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A fine-tuned Depth Anything V2 model for depth estimation, trained on forest canopy height data.
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---
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language: en
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license: apache-2.0
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tags:
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- depth-estimation
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- computer-vision
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- pytorch
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- absolute depth
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pipeline_tag: depth-estimation
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library_name: transformers
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---
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# Depth-CHM Model
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A fine-tuned Depth Anything V2 model for depth estimation, trained on forest canopy height data.
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