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
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README.md
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# TIPSv2 — L/14 DPT Heads
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DPT (Dense Prediction Transformer) heads for depth estimation, surface normal prediction, and semantic segmentation
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## Usage
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- **Backbone**: [TIPSv2 L/14](google/tipsv2-l14) (loaded automatically)
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- **Heads**: ~102M total params (depth + normals + segmentation)
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- **Segmentation**: ADE20K, 150 classes
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- **Input**: images in `[0, 1]` range, any resolution (multiples of 14 recommended)
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# TIPSv2 — L/14 DPT Heads
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DPT (Dense Prediction Transformer) heads for depth estimation, surface normal prediction, and semantic segmentation on top of the frozen [TIPSv2 L/14](https://huggingface.co/google/tipsv2-l14) backbone. The backbone is loaded automatically. The depth and normals heads are trained on the NYU Depth V2 dataset and segmentation is trained on the ADE20K dataset (150 classes).
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## Usage
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- **Backbone**: [TIPSv2 L/14](google/tipsv2-l14) (loaded automatically)
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- **Heads**: ~102M total params (depth + normals + segmentation)
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- **Depth & normals**: NYU Depth V2
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- **Segmentation**: ADE20K, 150 classes
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- **Input**: images in `[0, 1]` range, any resolution (multiples of 14 recommended)
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