Depth Estimation
Transformers
Safetensors
tipsv2_dpt
feature-extraction
vision
surface-normals
semantic-segmentation
dense-prediction
custom_code
Instructions to use google/tipsv2-b14-dpt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/tipsv2-b14-dpt with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("depth-estimation", model="google/tipsv2-b14-dpt", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModel processor = AutoImageProcessor.from_pretrained("google/tipsv2-b14-dpt", trust_remote_code=True) model = AutoModel.from_pretrained("google/tipsv2-b14-dpt", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Remove image_encoder.py and update configuration_dpt.py
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Remove image_encoder.py and update configuration_dpt.py
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Remove image_encoder.py
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Update files for transformers integration
Verified signatureUpdates the repo to smoothly integrate with transformers. PR: https://github.com/huggingface/transformers/pull/46347 Changes: - Update `config.json` to match transformers structure - Update config classes to handle new `config.json` - Add processor and tokenizer configs - Update DPT `model.safetensors` files to include DPT head + backbone weights - Update DPT model code to handle extra weights in `model.safetensors` filea77a1ca
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Update example image URL to use HF-hosted ADE20K image
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Add navigation table linking all variants and DPT heads
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Revert "Use device-agnostic code instead of hardcoded .cuda()"
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Use device-agnostic code instead of hardcoded .cuda()
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Add print statements with descriptive comments to code examples
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Use cat photo, add print statements to code examples
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Fix zero-shot segmentation section, use public example image
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Fix backbone link in Model details
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Update README
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