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 AutoImageProcessor, AutoModel processor = AutoImageProcessor.from_pretrained("google/tipsv2-g14-dpt", trust_remote_code=True) model = AutoModel.from_pretrained("google/tipsv2-g14-dpt", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
Commit History
Remove image_encoder.py and update configuration_dpt.py 6f7faf2 verified
Remove image_encoder.py and update configuration_dpt.py 0caf79c verified
Remove image_encoder.py and update configuration_dpt.py b7ed313 verified
Remove image_encoder.py 9cb581a verified
Update files for transformers integration 4ef05df verified
Update example image URL to use HF-hosted ADE20K image 7eec622
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Add navigation table linking all variants and DPT heads 109ccc8
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Revert "Use device-agnostic code instead of hardcoded .cuda()" 071bca8
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Use device-agnostic code instead of hardcoded .cuda() cd27033
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Add print statements with descriptive comments to code examples 1cc1840
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Use cat photo, add print statements to code examples 28ae6d4
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Fix zero-shot segmentation section, use public example image e3015bd
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Fix backbone link in Model details 51a33d5
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Update README 76a1ecb
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