Instructions to use onkarsus13/ConFiDeNet-Large-VQ-32 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use onkarsus13/ConFiDeNet-Large-VQ-32 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, ConFiDeNetForDepthEstimation processor = AutoImageProcessor.from_pretrained("onkarsus13/ConFiDeNet-Large-VQ-32") model = ConFiDeNetForDepthEstimation.from_pretrained("onkarsus13/ConFiDeNet-Large-VQ-32", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from onkarsus13/ConFiDeNet-Large-VQ-32: direct link, hf CLI and curl.
- Browser
- Download file 282 Bytes
-
https://huggingface.co/onkarsus13/ConFiDeNet-Large-VQ-32/resolve/main/preprocessor_config.json
- Command line
-
hf download hf://onkarsus13/ConFiDeNet-Large-VQ-32/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/onkarsus13/ConFiDeNet-Large-VQ-32/resolve/main/preprocessor_config.json
282 Bytes
| { | |
| "do_normalize": true, | |
| "do_rescale": true, | |
| "do_resize": true, | |
| "image_mean": 0.5, | |
| "image_processor_type": "ConFiDeNetImageProcessorFast", | |
| "image_std": 0.5, | |
| "resample": 2, | |
| "rescale_factor": 0.00392156862745098, | |
| "size": { | |
| "height": 1536, | |
| "width": 1536 | |
| } | |
| } | |