Depth Estimation
Transformers
Safetensors
tipsv2_dpt
feature-extraction
vision
surface-normals
semantic-segmentation
dense-prediction
custom_code
Instructions to use google/tipsv2-g14-dpt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/tipsv2-g14-dpt with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("depth-estimation", model="google/tipsv2-g14-dpt", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("google/tipsv2-g14-dpt", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d884a3f2dc7272dbc9aecbd3da486a16deea5038c2ddff337d4a4cf68414ff8f
- Size of remote file:
- 738 MB
- SHA256:
- 637e1095c02ef28d35a516ad5d8e56d7b6fee3570bd58fd93986fbec20f4d1c8
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