Instructions to use hf-internal-testing/tiny-random-DepthAnythingForDepthEstimation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-DepthAnythingForDepthEstimation with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("depth-estimation", model="hf-internal-testing/tiny-random-DepthAnythingForDepthEstimation")# Load model directly from transformers import AutoImageProcessor, AutoModelForDepthEstimation processor = AutoImageProcessor.from_pretrained("hf-internal-testing/tiny-random-DepthAnythingForDepthEstimation") model = AutoModelForDepthEstimation.from_pretrained("hf-internal-testing/tiny-random-DepthAnythingForDepthEstimation") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ea8f715443e900d2760b8a5832ff31586a9d2199c57250b950473e80522cabc0
- Size of remote file:
- 39.5 kB
- SHA256:
- 2c5df933937780b103a7ea3d43d5a6d1beefb4d1fedbfc6b1269e7873b8df328
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.