Depth Estimation
Transformers
Safetensors
tipsv2_dpt
feature-extraction
vision
surface-normals
semantic-segmentation
dense-prediction
custom_code
Instructions to use google/tipsv2-so400m14-dpt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/tipsv2-so400m14-dpt with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("depth-estimation", model="google/tipsv2-so400m14-dpt", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModel processor = AutoImageProcessor.from_pretrained("google/tipsv2-so400m14-dpt", trust_remote_code=True) model = AutoModel.from_pretrained("google/tipsv2-so400m14-dpt", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Gabriele commited on
Commit ·
964153d
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Parent(s): 06ff717
Fix backbone link in Model details
Browse files
README.md
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## Model details
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- **Backbone**: [TIPSv2 SO400m/14](google/tipsv2-so400m14) (loaded automatically)
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- **Heads**: ~120M total params (depth + normals + segmentation)
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- **Depth & normals**: NYU Depth V2
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- **Segmentation**: ADE20K, 150 classes
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## Model details
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- **Backbone**: [TIPSv2 SO400m/14](https://huggingface.co/google/tipsv2-so400m14) (loaded automatically)
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- **Heads**: ~120M total params (depth + normals + segmentation)
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- **Depth & normals**: NYU Depth V2
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- **Segmentation**: ADE20K, 150 classes
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