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
- Xet hash:
- f97bb822c8942c863b8e6fbb95d1a341497e2f40cd97be1020856d7033eb5ce1
- Size of remote file:
- 632 MB
- SHA256:
- 718757c7c816630bbe2a9ac9bdce4ee3ec22f2e1ddbb32c838ff569dc15f88a9
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.