Depth Estimation
Transformers
Safetensors
tipsv2_dpt
feature-extraction
vision
surface-normals
semantic-segmentation
dense-prediction
custom_code
Instructions to use google/tipsv2-l14-dpt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/tipsv2-l14-dpt with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("depth-estimation", model="google/tipsv2-l14-dpt", trust_remote_code=True)# Load model directly from transformers import AutoImageProcessor, AutoModel processor = AutoImageProcessor.from_pretrained("google/tipsv2-l14-dpt", trust_remote_code=True) model = AutoModel.from_pretrained("google/tipsv2-l14-dpt", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
Commit History
Remove image_encoder.py and update configuration_dpt.py d378506 verified
Remove image_encoder.py and update configuration_dpt.py b00eec9 verified
Remove image_encoder.py and update configuration_dpt.py f112fdd verified
Remove image_encoder.py 8310656 verified
Update files for transformers integration 1ade58d verified
Update example image URL to use HF-hosted ADE20K image 690a850
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Add navigation table linking all variants and DPT heads 22d3549
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Revert "Use device-agnostic code instead of hardcoded .cuda()" d8d2fe7
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Use device-agnostic code instead of hardcoded .cuda() 4b9afcc
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Add print statements with descriptive comments to code examples df8dbf3
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Use cat photo, add print statements to code examples def9b0d
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Fix zero-shot segmentation section, use public example image b4afd52
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Fix backbone link in Model details cf52fa0
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Update README 64789f3
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