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)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("google/tipsv2-so400m14-dpt", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
Add arXiv tag to link the paper 36b73af verified
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Update example image URL to use HF-hosted ADE20K image 551339b
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Use device-agnostic code instead of hardcoded .cuda() a8bfb7d
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Use cat photo, add print statements to code examples 982002b
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Fix zero-shot segmentation section, use public example image 4482ee7
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Fix backbone link in Model details 964153d
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