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:
- a4027b8a5fadfb60672656ed67da549d04ee9a70520737ba0f031b44bea3137c
- Size of remote file:
- 39.5 kB
- SHA256:
- 9531c865a21d0fd9a9ae833af0129f637e06518937e7e3b6ba25098de617b4cb
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.