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
Download preprocessor_config.json from google/tipsv2-l14-dpt: direct link, hf CLI and curl.
- Browser
- Download file 261 Bytes
-
https://huggingface.co/google/tipsv2-l14-dpt/resolve/refs%2Fpr%2F1/preprocessor_config.json
- Command line
-
hf download hf://google/tipsv2-l14-dpt@refs/pr/1/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/google/tipsv2-l14-dpt/resolve/refs%2Fpr%2F1/preprocessor_config.json
261 Bytes
| { | |
| "do_convert_rgb": true, | |
| "do_normalize": false, | |
| "do_rescale": true, | |
| "do_resize": true, | |
| "image_processor_type": "Tipsv2DptImageProcessor", | |
| "resample": 2, | |
| "rescale_factor": 0.00392156862745098, | |
| "size": { | |
| "height": 448, | |
| "width": 448 | |
| } | |
| } | |