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)# 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
Remove image_encoder.py and update configuration_dpt.py
Browse filesRemoves the unused image_encoder.py file and uploads configuration_dpt.py.
- configuration_dpt.py +0 -1
configuration_dpt.py
CHANGED
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@@ -26,7 +26,6 @@ class TIPSv2DPTConfig(PretrainedConfig):
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num_depth_bins=256,
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min_depth=1e-3,
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max_depth=10.0,
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-
semantic_loss_ignore_index=255,
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**kwargs,
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):
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super().__init__(**kwargs)
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num_depth_bins=256,
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min_depth=1e-3,
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max_depth=10.0,
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**kwargs,
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):
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super().__init__(**kwargs)
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