Instructions to use hf-tiny-model-private/tiny-random-GLPNForDepthEstimation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-GLPNForDepthEstimation with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("depth-estimation", model="hf-tiny-model-private/tiny-random-GLPNForDepthEstimation")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForDepthEstimation processor = AutoImageProcessor.from_pretrained("hf-tiny-model-private/tiny-random-GLPNForDepthEstimation") model = AutoModelForDepthEstimation.from_pretrained("hf-tiny-model-private/tiny-random-GLPNForDepthEstimation", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from hf-tiny-model-private/tiny-random-GLPNForDepthEstimation: direct link, hf CLI and curl.
- Browser
- Download file 166 Bytes
-
https://huggingface.co/hf-tiny-model-private/tiny-random-GLPNForDepthEstimation/resolve/refs%2Fpr%2F1/preprocessor_config.json
- Command line
-
hf download hf://hf-tiny-model-private/tiny-random-GLPNForDepthEstimation@refs/pr/1/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/hf-tiny-model-private/tiny-random-GLPNForDepthEstimation/resolve/refs%2Fpr%2F1/preprocessor_config.json
166 Bytes
| { | |
| "crop_size": 64, | |
| "do_rescale": true, | |
| "do_resize": true, | |
| "image_processor_type": "GLPNImageProcessor", | |
| "resample": 2, | |
| "size": 64, | |
| "size_divisor": 32 | |
| } | |