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)# pip install -U transformers accelerate # 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, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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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` file1ade58d