Instructions to use apple/DepthPro-hf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use apple/DepthPro-hf with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("depth-estimation", model="apple/DepthPro-hf")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForDepthEstimation processor = AutoImageProcessor.from_pretrained("apple/DepthPro-hf") model = AutoModelForDepthEstimation.from_pretrained("apple/DepthPro-hf", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from apple/DepthPro-hf: direct link, hf CLI and curl.
- Browser
- Download file 280 Bytes
-
https://huggingface.co/apple/DepthPro-hf/resolve/main/preprocessor_config.json
- Command line
-
hf download hf://apple/DepthPro-hf/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/apple/DepthPro-hf/resolve/main/preprocessor_config.json
280 Bytes
| { | |
| "do_normalize": true, | |
| "do_rescale": true, | |
| "do_resize": true, | |
| "image_mean": 0.5, | |
| "image_processor_type": "DepthProImageProcessorFast", | |
| "image_std": 0.5, | |
| "resample": 2, | |
| "rescale_factor": 0.00392156862745098, | |
| "size": { | |
| "height": 1536, | |
| "width": 1536 | |
| } | |
| } | |